the development of a 48v, 10kwh lifepo4 battery management

The Development of a 48V, 10kWh LiFePO4Battery Management System for Low Voltage

Battery Storage Applications.

by

Bartholomeus van Wyk Horn

Thesis presented in partial fulfilment of the requirements forthe degree of Master of Engineering (Electrical) in the

Faculty of Engineering at Stellenbosch University

Department of Electrical and Electronical Engineering,University of Stellenbosch,

Private Bag X1, Matieland 7602, South Africa.

Supervisor: Dr. P.J. RandewijkDr. J.M. Strauss

March 2017

Declaration

By submitting this thesis electronically, I declare that the entirety of the work containedtherein is my own, original work, that I am the sole author thereof (save to the extentexplicitly otherwise stated), that reproduction and publication thereof by StellenboschUniversity will not infringe any third party rights and that I have not previously in itsentirety or in part submitted it for obtaining any qualification.

Date: March 2017

©Copyright c 2017 Stellenbosch University All rights reserved.

i

Stellenbosch University https://scholar.sun.ac.za

Abstract

The Development of a 48V, 10kWh LiFePO4 BatteryManagement System for Low Voltage Battery Storage

Applications.B.V. Horn

Department of Electrical and Electronical Engineering,University of Stellenbosch,

Private Bag X1, Matieland 7602, South Africa.

Thesis: MEng (Elec)December 2016

Renewable energy sources are a promising replacement for fossil fuels in future energygeneration. To fully replace fossil fuels some form of energy storage is required. Chemicalbatteries offer an energy storage solution that is flexible and scalable for applicationsranging from electric vehicles to residential and even commercial applications.

Lithium-ion (Li-ion) battery technologies offers the most promising performance interms of energy density, power density as well as cycle life. Unfortunately, Li-ion batteriesare very sensitive to usage outside of the specified operating range. These specifiedparameters include the battery operating temperature, over- and undervoltage thresholdsas well as the maximum charge and discharge current. A Battery Management System(BMS) is thus required to monitor all of the above mentioned parameters and to ensurethe battery is operated safely and within the specified range.

A BMS’s primary focus is on the safety and protection of the battery, to minimise therisk of sudden failure and to maximise the life of the battery. The secondary function ofthe BMS is to perform battery diagnostics which could be used for more effective energymanagement of the battery.

The objective of this project was to develop a BMS to be used within a Li-ion batterypack for a micro electric vehicle. The developed BMS was used for battery testing.The battery results were used to estimate the battery parameters off-line according toa specific battery model. The estimated battery parameters can be used as a basis forfuture energy management purposes. An on-line parameter estimation algorithm wasalso developed. The algorithm was proven to be successful with a simulation. Futurework is required in order to simplify the practical implementation of the algorithm.

Another objective of this project was to development a solid state contactor (SSC)that can be used to disconnect the battery from a load. Mechanical contactors, whichpresents some disadvantages, are typically used for high current applications. The proof

ii


ABSTRACT iii

of concept SSC was proven to be an efficient though costly substitute to replace themechanical contactor within the BMS design.


Uittreksel

Die Ontwikkeling van ’n 48V, 10kWh LiFePO4 Battery BestuurStelsel vir Lae Spanning Battery Stoor Toepassings.

B.V. HornDepartement Elektries en Elektroniese Ingenieurswese,

Universiteit van Stellenbosch,Privaatsak X1, Matieland 7602, Suid Afrika.

Tesis: MIng (Elek)Desember 2016

Hernubare energie bronne is belowende opsies om fossiel brandstof bronne mee te vervang.Om fossiel brandstowwe volledig te vervang is daar een of ander vorm van energie storingnodig. Chemiese batterye bied so ’n oplossing wat buigsaam en maklik skaleerbaar isvir toepassings wat strek van elektriese voertuie tot residensiële en selfs kommersiëleverbruik.

Lithium-ioon battery tegnologië bied belowende verrigting in terme van energie digt-heid, drywings digtheid en lewens siklusse. Ongelukking is hierdie tegnologië baie sensitiefom buite die vervaardiger se spesifikasies bedryf te word. Hierdie spesifikasies sluit in diebattery temperatuur, oor- en onderspannings drumpel asook die maksimum battery laaien ontlaai stroom. ’n Battery monitor stelsel (BMS) word gebruik, vir hierdie rede, omal hierdie spesifikasies te meet en te verseker dit is binne die veilige venster van gebruik.

’n BMS se primêre doel is die beveiliging en die beskerming van die battery om die lewevan die battery te maksimeer. Die sekondêre doel van die BMS is om battery diagnosete doen vir meer effektiewe energie bestuur.

Die doel van hierdie projek is om ’n BMS te ontwikkel vir ’n mikro elektriese voertuig.The ontwikkelde stelsel was gebruik om battery toetse te doen. Die resultate was gebruikom die battery parameters van ’n battery model af te skat. Hierdie afgeskatte parameterskan in die toekoms gebruik word vir energie bestuur doelwitte.

’n Aanlyn parameters afskatting algoritme word ook in hierdie projek ontwikkel. Diealgoritme word slegs bewys deur behulp van simulasies. Verdere werk sal gedoen moetword om die algoritme aan te pas om in ’n praktiese stelsel geïmplementeer te kan word.

Die projek ondersoek ook die onwikkeling van ’n “solid state contactor” wat gebruikkan word om die battery van die las te ontkoppel. Meganiese kontaktors, wat nadele het,word tipies gebruik vir hoë stroom toepassings. Die “solid state contactor” poog om ’nopsie te wees wat die meganiese kontaktor in die hoof BMS ontwerp kan verwag.

iv


Acknowledgements

The author gratefully acknowledges the contributions of the following individuals andinstitutions:• My Mother and Father who have supported the author both financially and men-

tally.

• My two supervisors, Dr Randewijk and Dr Strauss, for all the time spent helpingto solve problems.

• The NRF who invested in the project.

• Mr Arendse for helping with some of the soldering work.

• All the post graduate students sitting in the power electronics laboratory includingAdel Coetzer.

• The Mellowcabs team for all the support during the project.

v


Contents

List of Figures viii

List of Tables x

Nomenclature xi

1 Project Overview 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Project Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Literature Study 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Batteries: A Short Overview . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Battery Management Systems . . . . . . . . . . . . . . . . . . . . . . . . 102.4 Battery Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.5 Off-line Parameter Identification of an ECM . . . . . . . . . . . . . . . . 172.6 State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.7 Recursive Least Squares method . . . . . . . . . . . . . . . . . . . . . . . 232.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3 Hardware design 253.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Proof of Concept Battery Management System . . . . . . . . . . . . . . . 253.3 Full Scale Battery Management System . . . . . . . . . . . . . . . . . . . 303.4 Prototype Solid State Contactor . . . . . . . . . . . . . . . . . . . . . . . 403.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4 Software Design 474.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3 Main loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.4 Battery Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.5 Current sense loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.6 Master control loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

vi


CONTENTS vii

5 On-line Parameter Estimation 545.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2 Battery model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.3 Recursive Least Squares Algorithm . . . . . . . . . . . . . . . . . . . . . 575.4 Simulation and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

6 Results 666.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666.2 Battery Management System . . . . . . . . . . . . . . . . . . . . . . . . 666.3 Battery Off-line Parameter Estimation . . . . . . . . . . . . . . . . . . . 736.4 Solid State Contactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7 Conclusion 857.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Bibliography 88

Appendices 92

A Calculations 93A.1 LM5017 regulator design . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

B Code 96B.1 Python USB listener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96B.2 MATLAB Symbolic solver . . . . . . . . . . . . . . . . . . . . . . . . . . 97B.3 Noise filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98B.4 Battery balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

C PCB Schematics 102C.1 Full Scale BMS Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . 102


List of Figures

2.1 Ragone diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Diagram illustrating the discharge of a LFP cell . . . . . . . . . . . . . . . . 82.3 Requirement fulfilment of various cathode materials . . . . . . . . . . . . . . 92.4 Possible battery pack cell configurations . . . . . . . . . . . . . . . . . . . . 102.5 Typical passive cell balancing topology . . . . . . . . . . . . . . . . . . . . . 132.6 Internal resistance equivalent circuit model . . . . . . . . . . . . . . . . . . . 152.7 Single polarisation Thévenin equivalent circuit model . . . . . . . . . . . . . 152.8 PNGV equivalent circuit model . . . . . . . . . . . . . . . . . . . . . . . . . 152.9 Dual polarisation Thévenin equivalent circuit model . . . . . . . . . . . . . . 162.10 RC equivalent circuit model . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.11 Typical discharge OC voltage test . . . . . . . . . . . . . . . . . . . . . . . . 182.12 Typical open circuit hysteresis . . . . . . . . . . . . . . . . . . . . . . . . . . 192.13 Battery voltage measured during off-line test . . . . . . . . . . . . . . . . . . 202.14 Block diagram of the ECM SOC Estimation Methods . . . . . . . . . . . . . 23

3.1 Proof of concept BMS circuit diagram . . . . . . . . . . . . . . . . . . . . . 263.2 Proof of concept BMS with the SEM battery . . . . . . . . . . . . . . . . . . 273.3 Voltage ripple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.4 Full scale BMS circuit diagram . . . . . . . . . . . . . . . . . . . . . . . . . 313.5 Full scale battery pack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.6 Digital model of the battery pack . . . . . . . . . . . . . . . . . . . . . . . . 333.7 Main control BMS PCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.8 TPS54060 voltage ripple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.9 Balance circuit diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.10 Balancing PCB connected to cell terminals . . . . . . . . . . . . . . . . . . . 373.11 Current sensor circuit diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 393.12 Current sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.13 Solid state contactor circuit diagram . . . . . . . . . . . . . . . . . . . . . . 433.14 Dead time design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.15 Manufactured solid state contactor . . . . . . . . . . . . . . . . . . . . . . . 45

4.1 Main flow diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.2 Cell balancing flow diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.3 Current sensing flow diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 514.4 Master flow diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.1 Single polarisation (SP) Thévenin model . . . . . . . . . . . . . . . . . . . . 55

viii


LIST OF FIGURES ix

5.2 Dual polarisation (DP) Thévenin model . . . . . . . . . . . . . . . . . . . . 565.3 Simulink simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.4 Battery discharge profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.5 Estimated Voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.6 Voltage estimation error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.7 Estimates of the RLS algorithm compared to the actual parameters . . . . . 635.8 τt2 quantization error result . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

6.1 ADC error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.2 Voltage measurement noise comparison . . . . . . . . . . . . . . . . . . . . . 686.3 Current sensor noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696.4 Filtered current sensor noise . . . . . . . . . . . . . . . . . . . . . . . . . . . 696.5 Current sensor thermal performance . . . . . . . . . . . . . . . . . . . . . . 706.6 Weak terminal connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716.7 Cell balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726.8 Impact of protection fuse during cell balancing . . . . . . . . . . . . . . . . . 726.9 Dual polarization Thévenin equivalent circuit model . . . . . . . . . . . . . 736.10 Pulse discharge test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746.11 SOC vs OC voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756.12 Ohmic resistance characteristic curve of the battery . . . . . . . . . . . . . . 756.13 Curve fit of dynamic behaviour . . . . . . . . . . . . . . . . . . . . . . . . . 766.14 Dynamic resistance characteristic curve of the battery . . . . . . . . . . . . . 766.15 Total battery resistance characteristic curve . . . . . . . . . . . . . . . . . . 776.16 Time constant τt1 characteristic curve . . . . . . . . . . . . . . . . . . . . . . 786.17 Time constant τt2 characteristic curve . . . . . . . . . . . . . . . . . . . . . . 786.18 SSC test set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796.19 SSC current at maximum load . . . . . . . . . . . . . . . . . . . . . . . . . . 806.20 Current sense and reference pin at maximum load . . . . . . . . . . . . . . . 806.21 SSC temperature at maximum load . . . . . . . . . . . . . . . . . . . . . . . 816.22 SSC trip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826.23 Current sense and reference pin . . . . . . . . . . . . . . . . . . . . . . . . . 836.24 Dead time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836.25 SSC during turn-off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

B.1 Noise of prototype BMS compared to that of the up-scaled BMS . . . . . . . 99B.2 Battery balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100B.3 Battery balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100B.4 Battery balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

C.1 Balance schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106C.2 Current sense schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107


List of Tables

3.1 LP55100100 cell specifications . . . . . . . . . . . . . . . . . . . . . . . . . . 263.2 LY-100AH cell specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.3 CSD19535KTT Power MOSFET specifications . . . . . . . . . . . . . . . . . 42

5.1 Simulink model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

x


Nomenclature

List of symbolsV VoltA AmpereAh Ampere hourQ CapacityR ResistanceC CapacitanceL InductanceW Wattω Frequency

Abbreviations & AcronymsADC Analogue to Digital ConverterBMS Battery Management SystemCAN Controller Area NetworkCC Constant CurrentCPU Central Processing UnitCRC Cyclic Redundancy CheckCV Constant VoltageDC Direct CurrentDP Dual polarisationECM Equivalent Circuit ModelEIS Electrochemical Impedance SpectroscopyEMI Electromagnetic InterferenceESD Electro Static DischargeEV Electric VehicleFET Field-Effect TransistorFF Forgetting FactorFTDI Future Technology Devices InternationalHPPC Hybrid Pulse Power CharacterisationIGBT Insulated-Gate Bipolar Transistor

xi


NOMENCLATURE xii

I2C Inter-Integrated CircuitIIR Infinite Impulse ResponseLCO Lithium Cobalt OxideLFP Lithium Iron PhosphateLi LithiumLMO Lithium Manganese OxideLNMC Lithium Nickel Manganese Cobalt OxideLNCA Lithium Nickel Cobalt Aluminium OxideLS Least SquaresLTO Lithium TitanateMCU Micro Controlling UnitMOSFET Metal-Oxide-Semiconductor Field-Effect TransistorNiMH Nickel-Metal HydrideNTC Negative Temperature CoefficientOC Open CircuitOV Over VoltagePNGV Partnership for a New Generation of VehiclesRLS Recursive Least SquaresSC Short CircuitSEM Shell-Eco MarathonSOC State Of ChargeSOH State Of HealthSOP State Of PowerSP Single PolarisationSPI Serial Peripheral InterfaceSSC Solid State ContactorTI Texas InstrumentsTVS Transient Voltage SuppressorUDDS Urban Dynamometer Driving ScheduleUSB Universal Serial BusUV Under Voltage


Chapter 1

Project Overview

1.1 IntroductionFossil fuels are currently used to generate electricity and to provide fuel for transportation.It has the advantage that the energy is stored in chemical bonds which can be utilisedwhenever necessary and has a very high energy density compared to other storage options.

Renewable energy sources are a promising replacement for fossil fuels, since no green-house gasses are released during the energy generation process. The cost of renewabletechnologies has decreased significantly [1] and the need for fossil fuel replacements areincreasing as the effects of global warming are becoming more and more apparent. Un-fortunately, these sources come with other difficulties.

One such difficulty is that renewable energy sources require the energy generated tobe utilised immediately after generation. A significant drawback with most renewabletechnologies is that they cannot continuously supply a baseload. Without large energystorage devices connected to renewable energy sources, the energy generated is eitherunpredictable (wind power) or it is periodical (solar power). Thus, some sort of storagedevice is required to supply power while power fluctuations in the renewable sources arepresent.

Energy storage options that are currently available for commercial implementationinclude hydro systems, thermal energy storage and chemical batteries. Unfortunately,hydro systems are depended upon the location of the system. Thermal energy storageis typically used at concentrated solar power plants where energy is stored thermallyin a storage medium. The thermal energy is converted at a later stage to electricalenergy by means of a turbine. Unfortunately, this technique is not ideal for renewableenergy sources that directly generates electrical power, due to the inefficient processof converting thermal energy into electrical energy. It is clear that none of the abovementioned technologies can be practically implemented for small scale energy storagesuch as required by electric vehicles or residences. Chemical batteries offer an energystorage solution that is flexible and scalable for applications ranging from electric vehiclesto residential and even commercial energy storage.

Currently battery storage options are still expensive, but the price is gradually de-creasing. This decrease can be attributed to more effective manufacturing processes aswell as economies of scale. The battery price is moving towards the point where it isbecoming a viable solution for either electric vehicles (EV) or peak power shaving. The

1


CHAPTER 1. PROJECT OVERVIEW 2

latest research on battery technologies are promising. The current commercially availablebattery technologies, in terms of performance, are good enough to replace fossil fuels asan energy storage solution.

Lithium-ion (Li-ion) battery technologies offers the most promising results in terms ofenergy density, power density as well as cycle life. Li-ion battery technologies include awide range of different battery chemistries, all with the similarity that Li-ions are used tocarry the positive charge between the two electrodes of the battery. Li-ion batteries havemany advantages compared to batteries with other chemistries, which will be discussedin more detail in Chapter 2.

Unfortunately, Li-ion batteries are very sensitive to being used outside of its specifiedoperating range. These specifications include the battery operating temperature, over-and undervoltage thresholds as well as the maximum charge and discharge current. Thisis due to the fact that Lithium is a very reactive element. The Li-ion cells can potentiallyignite or even explode if it is used outside of its safe operating range specified by themanufacturer [2]. A Battery Management System (BMS) is required, for this reason,to monitor all of the above mentioned specifications and to ensure that the battery isoperated safely within the specified range.

The objective of this project is to develop a BMS for a Li-ion battery pack that willbe used on a micro electric vehicle (EV). This technology can also be applied to therenewable energy storage sector since the research of Li-ion battery storage for EVs aremore advanced than for renewable storage.

1.2 Project MotivationIn an attempt to reduce the high investment cost of a Li-ion battery it is crucial tomaximise the lifespan of the battery. One of the easiest ways to maximise the battery’scycle life is to use it within the specified operating range as indicated by the manufacturer.All the different aspects requires to be monitored continuously to ensure the battery isoperated within the specified range. This is achieved by using a BMS to monitor thebattery pack. The different cell voltages and temperatures, as well as the battery currentare measured by the BMS to ensure it is within the specified range of operation. TheBMS for instance disconnects the battery from the load if the resulting measurementsare not within the specified range of operation, effectively protecting the battery. Thisincreases battery performance by minimising the physical degradation of the battery.A BMS primary focus are therefore on the safety and the protection of the battery, tominimise the risk of sudden failure and to maximise the life cycle of the battery.

The secondary function of the BMS is to perform battery diagnostics, such as stateof charge (SOC) estimation, state of health (SOH) estimation and state of power (SOP)estimation. SOC is the amount of charge the battery has at a certain time, i.e. howmuch of the total energy capacity is available for usage. SOH refers to the current batterycapacity compared to the original capacity specified by the manufacturer. SOP refers tothe maximum amount of power that can be delivered by the battery at a specific time.These states cannot be measured directly, but can be estimated. Accurate estimationsof these states are very important for effective energy management. In order to estimatethese states accurately, a model is required to describe the dynamics of the battery.



A great variety of battery models exists. This project will investigate and select theoptimal model in terms of accuracy and complexity. Once the optimal model is selected,the parameters of the model will be estimated, which can in turn be used for energymanagement purposes.

1.3 Research ObjectivesThe motivations for this project as discussed above gives rise to the following objectives:

• Development of a proof of concept Li-ion BMS to demonstrate the basic workingof the BMS. This includes the design, manufacturing and testing of the proof ofconcept BMS.

• Development of a full scale Li-ion BMS for the purpose of a micro EV. This includesthe design, manufacturing and testing of the full scale BMS. The aim is to prolongthe life of the battery by ensuring the battery is operated in a safe and sustainableway.

• The successful implementation of a balancing algorithm within the BMS to max-imise the available capacity of the battery pack.

• Off-line parameter estimation using the BMS.

• The development of an on-line parameter estimation algorithm using the appropri-ate battery model. The algorithm will be proved by a simulation.

• Development of a proof of concept Solid State Contactor (SSC) to protect thebattery against overcurrent and short circuit conditions. This includes the design,manufacturing and testing of the proof of concept SSC.

1.4 Thesis Structure• Chapter 2: Literature Study

The relevant literature concepts and topics are discussed. The study describes,amongst others, related works, a short battery overview, BMSs, battery modellingand parameter estimation algorithms.

• Chapter 3: Hardware DesignThe hardware design of a proof of concept BMS is firstly discussed within thischapter. The design choices made to up-scale the system to a full scale BMS for anEV follows. Finally the design of a prototype solid state contactor are discussed inthis chapter.

• Chapter 4: Software DesignThe software design of the BMS is discussed in this chapter. This includes thedifferent monitoring loops and the balancing algorithm.



• Chapter 5: On-line Parameter EstimationAn on-line parameter estimation algorithm is investigated within this chapter. Asimulation is implemented to prove the accuracy of the algorithm.

• Chapter 6: ResultsThe test results of the designed proof of concept BMS, full scale BMS and proof ofconcept SSC are discussed in this chapter.

• Chapter 7: Conclusion and Future WorkA conclusion of the work presented within this thesis is discussed in this chapter aswell as some ideas for future work.


Chapter 2

Literature Study

2.1 IntroductionLi-ion batteries have the potential to significantly impact the energy storage sector. Thischapter discusses the history and operation of batteries in general but with a primaryfocus on Li-ion batteries. The surrounding concepts and principles used to monitorand manage batteries are also discussed within this chapter. It includes BMSs, batterymodelling, parameter estimation, state estimation and the recursive least squares method.

2.2 Batteries: A Short Overview

2.2.1 Introduction

The earliest forms of batteries are found in ancient civilisations, such as the Egyptiansand Parthians, where it was typically used for electroplating purposes, not storing energy.Batteries used for energy storing purposes were first investigated in the late 17th century.Alessandro Volta discovered in the year 1800 that two different metals joined together bya moist intermediary would generate a flow of electrical power when the two metals areconnected by a conductor. This discovery led to the invention of the first voltaic cell. Abattery is simply a set of cells in series.

In the two centuries that have pasted since, various new technologies have been devel-oped. The basic operation is however the same for all of these technologies, ranging fromnon-rechargeable to rechargeable batteries. Also, these technologies served an integralpart of society but because of the low specific energy and low specific power constraints,was never used as a mainstream energy storage medium. Specific energy (Wh/kg) is thenominal battery energy per unit mass, sometimes referred to as the gravimetric energydensity while specific power (W/kg) is the maximum available power per unit mass.

In 1912, experimentation started on Li-ion batteries. Only in 1970 was the first Li-ion batteries made available to the open market. It is clear that the development ofbatteries is slow and comes with a number of difficulties. In the past 40 years significantimprovements have been made to the initial Li-ion technology. Li-ion batteries nowadayshave the potential of high specific power and high specific energy.

The following section discusses the basic operation of batteries in more detail and alsoprovides a comparative study in terms of the performance of the commercially available

5


CHAPTER 2. LITERATURE STUDY 6

battery technologies.

2.2.2 Battery Operation

Batteries store electrical energy in chemical bonds. All batteries makes use of electro-chemical reactions referred to as reduction and oxidation reactions [3]. Reduction is thegain of electrons by a molecule, atom, or ion. Oxidation is the loss of electrons by amolecule, atom, or ion. A battery cell has three essential components: the anode, thecathode, and the electrolyte. The materials used for each of these components determinethe battery’s characteristics, but the basic working principle of a cell stays the same.

The anode and cathode materials are chosen in such a way that the anode donateselectrons, and the cathode accepts electrons. The tendency of a material to donate oraccept electrons is commonly expressed as the material’s reduction potential. Reductionpotential is measured in Volts (V). Each material has its own intrinsic reduction potential.The higher the potential, the greater the material’s tendency to be reduced.

The difference between the reduction potentials of the cathode and the anode deter-mines the nominal operating voltage of the cell. The anode and cathode are separatedby the electrolyte, which is typically a liquid or gel that conducts electricity. When theanode and cathode are connected to each other through a conductor, the anode undergoesa chemical reaction with the electrolyte in which it loses electrons, creating positive ions(oxidation). The positive ions flows through the electrolyte to reach the cathode. At thecathode, the positive ions and electrons reacts with the cathode (reduction). Togetherthe entire process is known as a redox reaction.

Lithium is the metal with the lowest density [4], the greatest reduction potential andthe highest energy-to-weight ratio. Therefore, it is at the forefront of battery technologiesand it will contribute extensively to the future of energy storage on a large scale. In reality,Li-ion batteries have a much higher energy density than other common rechargeable bat-teries, such as a nickel-metal hydride (NiMH) or lead-acid batteries. The Ragone diagramcharacterises the specific power and specific energy of the different battery chemistries.It is used to compare the different technologies and can be seen in Figure 2.1.

The Li-ion batteries are not displayed as a continuous range on the Ragone diagrambecause different cell technologies are used in energy storage cells compared to powerstorage cells. Some Li-ion technologies have the highest specific energy (Wh/kg) whileother Li-ion technologies have the highest specific power (W/kg). Other Li-ion technolo-gies tries to find a good balance between the two. It is important to note that typicallythere is a trade off between the specific energy and specific power, e.g. a lead-acid bat-tery’s capacity is greatly influenced by the speed at which the power is extracted from thebattery. This is one of the reasons why lead acid cells is not ideally suited for EV appli-cations. EV applications require a high specific energy, but also a relatively high specificpower. The combination of both these properties is one of Li-ion’s greatest advantages.The technology is, at the moment, expensive compared to other chemistries. This willchange in the future as the scale of production of Li-ion batteries increases which willlead to price decreases.

Li-ion batteries usually uses a mixture of different cathode and anode materials tocomplement the advantages and disadvantages of the respective materials. The redoxreaction for charging and discharging a Lithium Iron Phosphate (LFP) cell can be seen



Specificpo

wer,c

elllevel

[W/k

g]

Super-capacitor

Specific energy, cell level [Wh/kg]

1

10

100

1000

10000

100000

0 20 40 60 80 100 120 140 160 180 200

Lead-acidNi-Cd Ni-MH Na-NiCL

Lead-acidspirally wound

Li-ionvery high power

Li-ionhigh power

Li-ionhigh energy

Li-polymer

Figure 2.1: Ragone diagram [5]

below with a typical example of the operation of Li-ion batteries.

Discharge:

Anode: LiC6 −→ C6 + Li+ + e− (2.2.1)

Cathode: FePO4 + Li+ + e− −→ LiFePO4 (2.2.2)

Charge:

Anode: LiFePO4 −→ FePO4 + Li+ + e− (2.2.3)

Cathode: C6 + Li+ + e− −→ LiC6 (2.2.4)

A discharge diagram of a LFP cell can be seen in Figure 2.2. The oxidation andreduction reactions can be seen at the anode and cathode respectively. The positivedenoted terminal is at the cathode while the negative denoted terminal is at the anode.

During the charge cycle of a cell the redox reactions are reversed from that of thedischarge cycle. Oxidation takes place at the positive terminal and reduction at thenegative terminal. The positive terminal becomes the anode and the negative terminalbecomes the cathode.



Electrolyte

CathodeFePO4 + Li+ + e− → LiFePO4

e−

Li+

AnodeLiC6 → C6 + Li+ + e−

- +

Figure 2.2: Diagram illustrating the discharge of a LFP cell [6]

The most common material used for the anode is some form of carbon. Carbonmaterials have the advantage that their mechanical and electrical properties are not sig-nificantly affected by accepting or donating large amounts of lithium. Carbon is typicallyused in some form of graphite, but other materials do exist for example Lithium Titanate(LTO).

The chosen cathode material varies greatly for the different cell technologies. It ischosen according to the requirements of the application. Typical examples of currentlyused positive electrode materials is shown in Figure 2.3. The main determining factorsof batteries are performance, lifetime, safety, cost, power and energy density. The deter-mining factors of various battery technologies is also shown in Figure 2.3. The differentbattery technologies are rated according to these different factors. Generally, the batterytechnology is named after its positive electrode’s composition.

Starting from the left, Lithium Iron Phosphate (LFP) is well balanced with high safetyand cycle life. LFP has a relatively low specific energy because of its low voltage plateau.

Lithium Cobalt Oxide (LCO) is a chemistry that is typically used in consumer elec-tronics. Drawbacks include low thermal stability and relatively low cycle life.

Lithium Nickel Manganese Cobalt Oxide (LNMC) is widely used in EVs because it isvery well balanced and has a high specific power.

Lithium Manganese Oxide (LMO) shows high safety performance at a relatively lowcost since no expensive metals are used. Unfortunately, it is very sensitive to high tem-peratures and it has a relatively low specific power.

Lithium Nickel Cobalt Aluminium Oxide (LNCA) has the highest specific energy andspecific power. Unfortunately, it lacks in terms of safety and cost.

The variations on all of these battery technologies can differ greatly to result in a



LMO

LFP

LNMC

LCO

LNCA

Energy density

Costs

Safety

Cycles

Powerdensity

SOC-operatingrange

Figure 2.3: Requirement fulfilment of various cathode materials [7]

battery with the needed requirements for a specific application. There is no clear optimalsolution for battery chemistries, only chemistries that suite certain applications betterthan others.

Despite all the advantages it has, Li-ion technologies do have some disadvantages. Li-ion batteries degrade over time that leads to an increase in the internal resistance, whichdecreases the battery’s ability to deliver power. It is also susceptible to a number of otherpotential problems including oxygen production due to overcharging at the cathode andoverheating of the anode. These potential problems can lead to battery degradation orin worst-case scenarios, ignite the battery. Thus, it is crucial for Li-ion batteries to havea BMS to prevent these problems.

This section, thus far, has given a short review of batteries by discussing batteryoperation and comparing the different battery technologies. The rest of this sectiondiscusses different battery configurations since this greatly influences the specificationsof the system monitoring the battery.

2.2.3 Battery Configuration

Battery packs are a combination of cells. The voltage of a single cell is typically lowcompared to the demanded voltage for various applications. Cells are stacked in series todeliver a higher voltage to a load. This reduces the current, which minimises losses. Thecells can be configured in various different configurations as shown in Figure 2.4.

The arrangement of cells is dependant upon the following factors:

• The mechanical layout of the battery pack for structural integrity.

• The wiring harness connecting the different cells.



a) b) c) d)

Figure 2.4: Possible battery pack cell configurations: a) series, b) matrix, c) series-paralleland d) mixed

• Temperature management. How to best regulate the battery temperature.

• Robustness of the battery pack. By connecting the cells in a series-parallel configu-ration, if one cell fail, that series string could be disconnected by a contactor. Thiswould ensure that the rest of the pack could still be used.

• Li-ion cell voltages need to be operated within the specified range of operation forsafety purposes and to prolong the cell’s lifespan. To ensure this is the case, all thecell voltages needs to be monitored continuously. Connecting the cells in a matrixconfiguration reduces the amount of cells that needs to be monitored, effectivelysimplifying the BMS.

These various configurations have different advantages and disadvantages and wouldtypically depend upon the application of the battery. The battery configuration alsoinfluences the choice of BMS used to monitor it. The following section investigates BMSsin more detail.

2.3 Battery Management Systems

2.3.1 Introduction

The first priority of a BMS is to monitor the health of all the cells in the battery pack,while still being able to deliver the power required by the application. In order to prolongthe the life of the battery pack the BMS needs to maintain all the cells within the specifiedoperating range. In this section an overview of BMSs are presented. This includes BMSrequirements, architectures and battery balancing techniques.

2.3.2 Requirements

The BMS is an integral part of any large scale battery pack and is generally responsiblefor:



• Cell protection: Safety is the first priority of the BMS. Protecting the cells fromoperating outside of the manufacturer’s specified operating conditions are crucial.The rest of the system also needs to be protected from the battery in the event ofbattery failure.

• Data acquisition: Battery current, voltage and temperature measurements.

• Data analysis: State of power, state of health and state of charge estimation.

• Control: Charge and discharge current control to ensure it is within the manufac-turer’s specified operating conditions.

• Communication: Used to interact with other components in the system, e.g. thecharger or inverter. Typically also used to give users access to the data of thebattery.

• Battery balancing: Ensures that the maximum capacity of the battery is availablefor use.

2.3.3 Architectures

There are a variety of different BMS architectures. All of them have their advantagesand disadvantages. The architectures are typically set apart by the scalability and costof each system [8]. Some of the BMS architectures are presented next.

Centralised BMS Architecture: A centralised BMS architecture uses one main con-trol board that monitors all the different aspects of the battery. This architecture has theadvantage of low cost and easy implementation when used with a battery with a low cellcount. Disadvantages include large wiring harnesses as the size of the battery increases,since the main board needs to be connected to all the different cells and temperaturesensors. This also increases the complexity of the system as the cell count increases.Typically this architecture is ideally suited for battery packs with low cell counts.

Distributed BMS Architecture: A distributed BMS architecture has a node on eachcell which monitors the voltage and temperature of that specific cell. This node is a slavedevice which are connected to a master via a serial connection. All the different nodescommunicate its measurements via the serial connection to the master controller. Themaster interprets this data and controls the output of the battery accordingly. This archi-tecture has the advantage that it is very easily scalable and easy to install. Unfortunately,the cost of a distributed system can be relatively high compared to a centralised system.

Modular BMS Architecture: A modular BMS architecture is a combination of thecentralised and distributed architectures. It uses a set of slave devices that monitorsmore than one cell each. These slave devices are typically connected on top of each other,through a daisy chain communication interface, to monitor a large number of cells. Themodular BMS architecture is flexible and scalable. The slaves are controlled by a mastercontroller. The modular architecture delivers a good trade-off between the centralised



and distributed architectures. It is generally used for batteries containing a high numberof cells such as the batteries used for electric vehicles.

2.3.4 Battery Balancing

Cell balancing is used to ensure that the state of charge of each of the series connectedcells is even. The inconsistencies in the manufacturing of battery cells result in uniqueperformance characteristics for each individual cell in a battery pack. For this reasoncells accept and deliver charge at a slightly different efficiency and their overall capacitydiffers slightly. This small difference in efficiency and capacity results in one cell havinga higher SOC than another cell, even though the current through the series connectedcells are the same. These differences are aggravated during extensive use of a batterypack. The difference between the SOC of the individual cells continue to increase whichin turn lowers the overall capacity of the battery pack as a whole, since the battery packwill operate at the level of the weakest cell. The unbalanced state of the battery packcauses some of the cells to be operated in an overvoltage or undervoltage state which willsignificantly decrease the life cycle of the battery pack. It is therefore very importantthat the cells are balanced properly. There are two battery balancing techniques thatwill be discussed in the following subsections: passive and active cell balancing.

Passive Balancing: Passive balancing makes use of resistors to remove charge fromthe cells with higher cell voltages than the rest in the series string, until all the cellvoltages equal each other. The advantage is that the system has a relatively low costand complexity. The drawback of this method is that it is energy inefficient, since all theenergy removed from the higher cells are dissipated in resistors.

Passive balancing typically only balances the battery pack during charging. Duringdischarge, the whole battery pack’s capacity is constrained by the cell with the lowestcapacity in the series connected string. Balancing during discharge is not desirable sincethe energy is wasted.

A typical passive cell balancing topology is shown in Figure 2.5. Cell balancing ofCell1 can be achieved by closing switch S1 and S2. The resistance R1 determines thebalancing current and effectively the speed at which balancing is performed. ChoosingR1 depends on the cell chemistry (nominal voltage), the manufacturer’s tolerance of thecells and the application of the battery.

Active Balancing: Active balancing removes charge from a cell with a high state ofcharge and delivers it to a cell with a lower state of charge. Transformers, inductors orcapacitors are used as the active component to store the charge from the higher stateof charge cells and then deliver it to the lower state of charge cells. Switching circuitsare used in combination with these active components to effectively spread the chargefrom one cell to another. Active cell balancing has the advantage over passive cell bal-ancing that cells can be balanced during charging as well as during discharging. Thiseffectively increases the capacity of the battery pack. Another advantage of active cellbalancing, compared to passive cell balancing, is its high energy efficiency since energy isnot dissipated within the resistors. The active cell balancing circuits are more expensiveto build and more complex to control than passive cell balancing. The overall complexity



−Cell3+

S3

R3Voltagemonitor

−Cell2+

S2

R2Voltagemonitor

−Cell1+

S1

R1Voltagemonitor

Figure 2.5: Typical passive cell balancing topology

of active cell balancing also reduces the total reliability of the system. There are manydifferent active cell balancing topologies, but they will not be discussed in detail sincepassive balancing was chosen for this thesis due to the complexity of active balancing.

The following section discusses the different battery modelling techniques.

2.4 Battery Modelling

2.4.1 Introduction

Battery modelling is an effective way in which to manage the battery by estimating theSOH, SOP and the SOC. Accurate estimations of these states are very important sinceit cannot be measured directly. In order to estimate these states accurately a model isneeded to describe the dynamics of the battery. Research on electric vehicles has increaseddramatically and therefore a wide range of different battery models exists. These modelscan typically be split into three different categories, which include the equivalent circuitmodel, first principle model and the empirical model. These models are discussed in thefollowing section.

2.4.2 First Principle Model

The first principle model are usually the first choice for battery analysis due to its accuraterepresentation of a battery’s physical properties. It models the battery as simply acollection of atoms bound together by electrochemical reactions. These reactions are theinteractions between electrons, which can be described by the basic laws of physics. Thefirst principle model attempts to analyse the battery through the atomic number and massof the battery’s chemical elements. The different physical characteristics of the batteryare modelled though several electrochemical models. Although these models can achievehigh levels of accuracy, they are computationally complex and time consuming becauseof the involved partial differential equations. The first principle models are not suitablefor control-oriented or real-time applications. It is typically used to investigate and



understand the physical properties of batteries in order to design new battery materials.The first principle model is not, for these reasons, discussed in further detail in this thesis.

2.4.3 Empirical Model

The empirical model uses data-based methods to model the dynamic behaviour of abattery. These models rely entirely on experimental data sets and have no insight intothe physical electrochemical processes within the battery. A large amount of empiricalmodels has been proposed for various purposes. The Peukerts formulation being the mostcommonly used battery model. It was expanded to capture the non-linear relationshipbetween the battery’s capacity and the discharge current under different temperatures [9].Other more intelligent data-based models include, neural networks [10] and fuzzy logic[11], which is typically used to estimate the states of batteries. These models require largeoff-line training data to estimate the model’s parameters. These models have shown highlevels of accuracy. The downside is that training has to be acquired off-line. The trainingdata could also prove to be a constraint. New applications require new training data inorder to determine how the battery will react to a specific application. This makes themodel’s accuracy directly proportional to the quality of the training data [12]. In addition,the empirical models always rely on a large amount of experimental observations, whichrequire a large amount of experimental and modelling.

2.4.4 Equivalent Circuit Models

The Equivalent Circuit Model (ECM) is a good trade off between the empirical and firstprinciple models. It delivers some physical insight into the battery as well as a goodcontrol-orientated basis. A large variety of ECMs exist and will be discussed in thesections below. These models use electric circuits to mimic the dynamic electrochemicalprocesses of a battery. The accuracy of the model typically depends upon the number ofelectrical elements used. Parameters in the model in turn depend on many factors, suchas the SOC, the temperature and the SOH. The presented models disregard self-dischargeand other factors with long time constants.

Internal Resistance model: The internal resistance model is the most simplistic ECMavailable and is shown in Figure 2.6. It consists of an ideal voltage source VOC and anequivalent series resistor Rs. The voltage source is used to model the open circuit voltageand the resistor is used to model the ohmic resistance of the battery.

Single Polarisation Thévenin model: The Single Polarisation (SP) Thévenin modelis the most commonly used battery model and is shown in Figure 2.7. It consists of anideal voltage source VOC , series resistor Rs and a parallel resistor-capacitor Rt1-Ct1 pair.Again,the voltage source is used to model the open circuit voltage. The resistor Rs modelsthe ohmic resistance and the parallel RC pair models the polarization voltage.

Partnership for a new Generation of Vehicles linearised model: The Partner-ship for a New Generation of Vehicles (PNGV) linearised model is used in the "Free-domCAR Battery Test Manual" which is a result of the FreedomCAR program which



−VOC+

Rs

+

−

V (t)

I(t)

Figure 2.6: Internal resistance equivalent circuit model

−VOC+

Rs

Rt1

Ct1

Vt1+ −+

−

V (t)

I(t)

Figure 2.7: Single polarisation Thévenin equivalent circuit model

−VOC+

RsCpb

+Vpb

−

Rp

Cp

Vp+ −+

−

V (t)

I(t)

Figure 2.8: PNGV equivalent circuit model

ended in 2003. The program set out to lay the groundwork for more energy efficientand environmentally friendly highway transportation technologies and was funded by theAmerican government [13]. The PNGV model is based on the SP Thévenin model andis shown in Figure 2.8. Again, the voltage source VOC is used to model the open circuitvoltage, Rs models the ohmic resistance and the parallel RC (Rp-Cp) pair models thepolarization voltage. Capacitance Cpb models the cumulative open circuit voltage changewith respect to current I(t).

Dual Polarisation Thévenin model: The polarisation characteristic of a Li-ion bat-tery consists of the concentration polarisation and the electrochemical polarisation. Inthe SP Thévenin model both these are modelled by one parallel RC pair. Unfortunately,this one pair can only model it to a certain extent. The Dual Polarisation (DP) Thévenin



model adds an RC parallel pair to the SP Thévenin model as shown in Figure 2.9. Thisexpands the modelling of the polarisation characteristics of the battery, which increasesthe model accuracy.

The DP Thévenin model is an improved SP Thévenin model and consists of a voltagesource (VOC), three resistors (Rs, Rt1, Rt2) and two capacitors (Ct1, Ct2). Again,thevoltage source VOC is used to model the open circuit voltage and Rs models the ohmicresistance. The first parallel RC (Rt1-Ct1) pair models the electrochemical polarizationand the second parallel RC (Rt2-Ct2) pair models the concentration polarization.

−VOC+

Rs

Rt1 Rt2

Ct1

Vt1+ −

Ct2

Vt2+ −+

−

V (t)

I(t)

Figure 2.9: Dual polarisation Thévenin equivalent circuit model

RC model: The RC model shown in Figure 2.10 was developed by the battery manu-facturer SAFT [14]. It comprises of two capacitors (Cb, Cc) and three resistors(Re, Rc,Rt). The capacitor Cb models the capacity of the battery and is therefore extremelylarge. The capacitance Cc represents the dynamic behaviour and is typically relativelysmall. The resistance Rt is known as the terminal resistance, Re is the end resistance andRc is the capacitor resistance.

Cb −Vb+

Re

Rc

Cc+

Vc−

Rt

+

−

V (t)

I(t)

Figure 2.10: RC equivalent circuit model

The following section discusses the different methods of estimating the parameters foran ECM.



2.5 Off-line Parameter Identification of an ECM

2.5.1 Introduction

ECMs are commonly used to model battery behaviour. Accurate estimations of thebattery model parameters have a direct correlation with the model’s performance. In thissection, an overview is given on the methods of off-line ECM parameter identification.

2.5.2 Open Circuit Voltage Identification

Determining an accurate estimation of the open circuit voltage is a crucial part of theimplementation of most ECMs. The open circuit (OC) voltage VOC is present in all butone ECM (RC) model. The OC voltage is typically measured as a function of the SOCand temperature of the battery. If the battery is not used for an extended period of time,the battery terminal voltage will equal the electrochemical potential of the battery, thusreaching equilibrium. This is the potential required to balance the difference betweenthe anode and cathode’s ability to gain or lose electrons. This state is also referred to asthe OC voltage. The OC voltage is determined at set intervals of the SOC and constanttemperature. The relationship between the SOC and OC voltage is not linear and is thusinterpolated by a polynomial.

A typical procedure to determine the OC voltage is as follows: the battery is fullycharged with a Constant Current-Constant Voltage (CC-CV) profile according to themanufacturer’s specification. The CC-CV charging profile is achieved by charging thebattery at constant current (CC) until the battery reaches its maximum voltage. Thebattery is then charged to maintain this maximum voltage until the charge current de-creases below the fully charged specification as detailed by the manufacturer. A restperiod follows to allow the battery to reach its equilibrium potential. The battery is thendischarged at constant current to 90% of its nominal capacity. Typically, the manufac-turer’s rated discharging current will be used as the reference for the amount of dischargecurrent. Another rest period is then inserted during which time the battery will againreach its equilibrium potential. This cycle of discharging (10%) and inserting a rest pe-riod continues until one of the cells reaches its minimum voltage which marks the end ofthe OC voltage test.

An example of the typical results obtained from this testing procedure is shown inFigure 2.11. The same process can also be followed to determine the OC voltage of thebattery whilst the battery is charging, however the battery must be drained beforehandto 0% capacity. The battery has to be charged with the same amount of current usedwith the discharging method. The battery is charged at constant current charging cycles,similar to the discharge cycles, where the battery is charged 10% and given a rest periodto reach its equilibrium potential. This is repeated until one of the cells reaches itsmaximum voltage and the charge OC voltage test is completed.

It is worthy to note that the charge and discharge test do not compare perfectly,since the equivalent potential of the battery is dependant upon the previous usage of thebattery [15]. An exaggerated example of the OC voltage hysteresis during charging anddischarging is shown in Figure 2.12. This non-linearity of the battery is caused by the



0 5000 10000 15000Time [s]

42

44

46

48

50

52

Volta

ge[V

]VoltageOC voltage

Figure 2.11: Typical discharge OC voltage test

electrochemical reactions within the battery. The magnitude of the hysteresis is typicallydependant upon the cell chemistry.



0 20 40 60 80 100State of charge [%]

42

44

46

48

50

52

Ope

nci

rcui

tVol

tage

[V]

DischargeCharge

Figure 2.12: Typical open circuit hysteresis

2.5.3 Time Domain Parameter Identification

Simple time dependant testing can be used to characterise a battery’s behaviour. This isparticularly important to enable ECM parameter fitting. In addition to the OC voltagetest detailed in the previous section, there is the Hybrid Pulse Power Characterisationtest (HPPC) that is used to estimate the characteristics of the current dependent ECMparameters. A HPPC test consists of a high current pulse charging and discharging thebattery for for a short duration of time. Normally, pairs of equal magnitude dischargeand charge current pulses are applied at different SOC operating points of the battery.Applying a HPPC test at different SOC operating points leads to a dynamic ECM whichdescribes the battery behaviour over the full SOC range of the battery. Variations of thistest can be applied in order to suit the application of the battery.

The parameters and OC voltage of an ECM can be identified simultaneously as de-tailed in [16]. A typical example of the test is shown in Figure 2.13. The voltage Vs is thepeak value of the linear part of the curve while Vt is the voltage value of the exponentialpart of the curve.

The voltage Vs is used to calculate the ohmic resistance of the battery pack. It canbe calculated by

Rs =VsIbat

, (2.5.1)

where Ibat is the battery current just before the rest period starts.The voltage Vt is used to estimate the dynamic behaviour of the battery. Curve fitting

is typically used to estimate an appropriate time constant or time constants to obtainthe ECM parameters. Curve fitting is only applied to the exponential part of the curve.



0 5000 10000 15000Time [s]

42

44

46

48

50

52

Volta

ge[V

]

Vs

Vt

Rest period

Figure 2.13: Battery voltage measured during off-line test

The curve fit in combination with the battery current, just before the rest period starts,contain enough information to estimate the polarisation characteristic of the battery.

2.5.4 Frequency Domain Parameter Identification

Electrochemical impedance spectroscopy (EIS) is a frequency domain testing procedurethat employs small amplitude signal perturbations to measure the impedance of thebattery at different frequencies. This test is typically done at different cell operatingpoints such as temperature, SOC and whether the battery is busy being charged ordischarged. The magnitude of the perturbations have to be small in order to assume thatat that specific operating point, the system is linear. During the test, the temperature ofthe battery is required to be regulated to ensure the results can be related to a specificoperating point. Batteries are inherently non-linear devices, so it is crucial that theseconditions are met to ensure that the results for one operating point do not overlap withother operating points.

EIS testing can be used to identify the parameters of an ECM. The response of thebattery, when tested at mid-frequency ranges, exhibits behaviour which can be describedusing RC circuit elements [17]. The low and high frequency testing ranges results inbehaviour which can be described by capacitive and inductive circuit elements.

The impedance of Li-ion batteries are very low (within the milliohm range) and can-not be easily and accurately measured using laboratory EIS systems. It is said thatcommercial EIS systems become less accurate when the impedance is below 0.1 Ω [18].Another possible problem is the mutual inductance of the cell cable and placement of theleads which can have a major effect on the EIS system’s performance. The duration ofthe test at low frequencies (1 mHz or less), can be a considerable drawback since it can



last more than several hours. It is for these reasons that frequency domain parameterestimation is not investigated further within this thesis.

2.6 State Estimation

2.6.1 Introduction

A battery is a complex non-linear electrochemical device. Only a small amount of param-eters can be measured such as the voltage, current and temperature. Energy managementis crucial to use the battery optimally and effectively. Some parameters that are neededfor accurate energy management, cannot be measured, but only estimated. These pa-rameters include the SOH, SOC, and SOP and will be discussed in more detail in thissection.

2.6.2 SOH Estimation

The degradation of a Li-ion battery is indicated by two factors. One, the battery’scapacity decreases, and two, the internal resistance increases over time. This changes thebattery’s behaviour and could cause failure or limit the battery’s ability such that it isnot able to function as designed for. It is for this reason that the health of the batteryshould be analysed. SOH can be defined by either comparing the remaining capacity ofthe battery to the original rated capacity or by the increase in the internal resistance ofthe battery. These two definitions are defined below by

SOHcap =Qactual

Qrated

x 100%, (2.6.1)

SOHres = 1 +Rrated −Ractual

Rrated

. (2.6.2)

In the first definition, Qrated refers to the manufacturer’s rated capacity and Qactual

refers to the measured remaining capacity of the battery. This can be calculated bydischarging the battery from 100% to 0% according to the manufacturer’s standard dis-charging method. This will typically determine the discharge rate as well as the nominaloperating temperature.

In the second definition, Rrated refers to the initial internal resistance of the battery.Typically, the manufacturer does not supply this information in the datasheet. Thereforeit is important that the parametrisation of the battery is done at the beginning of its lifecycle in order to estimate the internal resistance. Ractual refers to the determined internalresistance of the battery. Again, both these values are calculated at the specified nominaloperating temperature and discharge rate.

Currently, there is no definite consensus in the industry to how SOH is measured.It is for this reason that a SOH has to be chosen that signals the end of life (EOL)of a battery. The general definitions are that either the EOL of a Li-ion battery is atSOHcap < 80% [19] or when the SOHres <= 0 [20].



2.6.3 SOC Estimation

The SOC is determined by comparing the the remaining charge Q of a battery comparedto the nominal capacity Qrated of the battery. The SOH of the battery influences theSOC since the capacity decreases over time as can be seen in the following equations

SOCnominal =Q

Qrated

x 100%, (2.6.3)

SOCactual =Q

Qactual

x 100% =SOCnominalSOHcap

x 100%. (2.6.4)

The SOC serves as a measurement of the amount of energy that is still available for usewithin the battery. Compared to an internal combustion vehicle, the SOC can be viewedas the fuel gauge of the battery. It is important to note that as the battery degradesand the capacity reduces, the SOC needs to be updated accordingly. For example ina degraded battery, the SOCactual might differ significantly from the SOCnominal of anew battery and will consequently convey a very inaccurate estimate of the remainingcharge. Additionally, the temperature conditions and the nature of the load could alsosignificantly impact the SOCactual compared to the SOCnominal.

A wide range of SOC estimation methods exist, as stated earlier. The most arbitrarymethod for estimating the SOC, is by integrating the current of the battery such that

SOC(t) = SOC(t0) +1

Q

∫ t

t0

I(τ)dτ. (2.6.5)

This method is also known as coulomb counting and is widely used in the consumerelectronics market. Unfortunately, the method is very sensitive to the accuracy of thecurrent sensor as well as to the initial SOC of the battery since an error in the measure-ment will be integrated over time. Another problem is the limited bandwidth due to thesampling period. Sudden changes in current can therefore not be accurately measured,which further decreases the accuracy of this method.

Other SOC estimation methods are mostly based on the way in which the batteryis modelled, except empirical methods of estimation. Pure empirical models uses thecurrent, temperature and voltage measurements as inputs and delivers an estimate onthe SOP, SOH or SOC as outputs. Hybrid empirical and ECM methods have beenproposed as detailed in [11], where the equivalent circuit model is based on fuzzy logic. Ingeneral the selected model is combined with some form of estimation method to estimatethe states within the model. A first principle model in combination with an extendedKalman filter is an example of such a model and is described in more detail in [21].ECMs are the most predominantly used solution for SOC estimation in automotive andlarge-scale battery packs [7].

An ECM has the advantage that the algorithms and models can be reused easilyfor different cell chemistries, and it is also efficient and robust. Typically, an ECM isused as the basis for the estimation process. The model is given the measured currentof the battery as an input. A control system is used to change the states of the ECMin such a way that the difference between the output of the model and real systemis minimised (negative feedback), as shown in Figure 2.14. These estimation methodsincludes the Luenberger observer, sliding mode method, Kalman filter and Proportional



Batterymodel

−

+

Estimationmethod

Battery

MeasuredCurrent

Current

MeasuredVoltage

CalculatedVoltage

Figure 2.14: Block diagram of the ECM SOC Estimation Methods

integral observer [22]. One of the states that these estimators will estimate, typicallyinclude the open circuit voltage VOC of the battery. This can then be used to obtain anestimation of the SOC of the battery.

2.6.4 SOP Estimation

State of power (SOP) is the ability of the battery to deliver or accept power to or fromthe application. An accurate SOP estimate is useful in practical battery applicationssince it can be used to determine the available power and as such ensure the battery isnot overcharged or over-discharged. SOP is defined similarly as discussed in [23]. It caneasily be estimated by the use of an ECM. Using the SP Thévenin model as an example,the maximum discharge current can be calculated if all the parameters of the model isknown.

The SOP can be calculated with the maximum allowed discharge current, withoutdecreasing a cell’s terminal voltage to below the cell voltage as specified by the manufac-turer. Thus,

SOP =Vlimit(VOC − Vlimit)

Rs +Rt1

[W]. (2.6.6)

Equation 2.6.6 is derived using node analysis. The voltage Vlimit is the minimumallowed terminal voltage of the cell. The voltage VOC is the open-circuit voltage and(Rs + Rt1) is the total internal resistance of the cell. This method can also be usedto calculate the maximum allowed charging current of a cell. In this case Vlimit is themaximum cell voltage specified by the manufacturer.

2.7 Recursive Least Squares methodThe least squares (LS) method is a mathematical algorithm that minimises the sum of thesquares of the differences between observed values and estimated values. This method istypically used for curve-fitting and this can be also be applied to the dynamic behaviourof Li-ion batteries. The parameters of an ECM can be estimated by the LS methodin such a way that the error between the measured data and the model is minimised.



Unfortunately, the LS method can only be applied to a complete data set, which makesit unsuitable for real-time estimation purposes. This is where the recursive least squares(RLS) method comes in.

The RLS method is derived from the LS method and is usually used for real-timeestimation. The model is recursively updated as new data is processed using the leastsquares method. The RLS method for on-line parameter estimation will be discussed inmore detail in Chapter 5.

2.8 ConclusionIn this chapter, the different concepts pertaining to the objectives of this thesis were pre-sented. This includes a short overview of batteries, where the different technologies werecompared and different configurations were discussed. The needed BMS requirementsand architectures were also investigated. Battery modelling and parameter estimationmethodologies were discussed in detail. Definitive definitions for the different state esti-mations of batteries were also given.


Chapter 3

Hardware design

3.1 IntroductionThe hardware design of a proof of concept BMS for a small Li-ion battery pack is discussedwithin this chapter. This design serves as a prototype to prove the basic capabilities ofthe different subsystems within the BMS. This design is scaled up for the purpose of amicro EV and is also discussed in this chapter. This chapter also includes the designof a proof of concept solid state contactor that may be used as an alternative to themechanical contactor used in the full scale BMS design.

3.2 Proof of Concept Battery Management System

3.2.1 Introduction

In this section a proof of concept BMS is designed for a small Li-ion battery pack. Thisconcept design is inspired by a previous prototype BMS designed for the Shell Eco-Marathon (SEM) competition. The competition’s goal was to travel a certain distanceusing the least amount of energy possible. Energy efficiency was therefore extremelyimportant. The prototype EV was supposed to have competed in the Shell Eco-Marathonrace, but due to some technical difficulties with the electrical drive train, the EV wasunable to participate in the race.

The concept design is based on the battery pack used for the SEM competition.The design and specifications of the battery is described in more detail in the followingsubsections.

3.2.2 Design choices

The SEM battery pack consists of 13 EEMB LP55100100 Lithium polymer cells connectedin series. The cell specifications is shown in Table 3.1. The proof of concept BMS designfor this thesis is based on these specifications.

The nominal battery voltage of the complete battery pack is 48V. This voltage hasbecome the standard for low voltage energy storage systems since it is well within the safeoperating range which is limited to 60V. The human body has an internal resistance and

25


CHAPTER 3. HARDWARE DESIGN 26

Table 3.1: LP55100100 cell specifications

Parameter RatingNominal voltage 3.7VMaximum voltage 4.2VMinimum voltage 2.75VNominal capacity 6.5Ah at 1.3A

Maximum discharge current 13AMaximum charge current 6.5A

as the magnitude of the voltage increases, so does the current in the case of an electricshock. An electric current too large can affect biological tissue in a hazardous manner.

A BMS consists of multiple components. This typically includes a battery monitoringchip that measures the cell voltages and pack temperature. A current sensor is used tomeasure the battery current. A Micro Controlling Unit (MCU) is used to process thedata and accordingly control the the output of the battery by means of some sort of aswitch.

A block diagram of the proof of concept BMS is shown in Figure 3.1. In the figure,B1 to B13 are balancing circuits that are controlled via the battery monitoring chip whichis programmed with the MCU. The design includes a shunt resistor as a current sensor.Back to back power field-effect transistors (FET) are used as a solid state switch. A MCUis the master controller which controls the monitoring chip. The MCU is powered by alow power buck regulator. The proof of concept BMS with the SEM battery is shown inFigure 3.2. The design choices for all the main circuit components will be discussed inthe rest of this section.

+

48 V

Currentsensor Switch−

B1

B2

B13

Monitorchip

I2C Micro-controller

Unit3.3 V Low

PowerDC-DC

Figure 3.1: Proof of concept BMS circuit diagram



Figure 3.2: Proof of concept BMS with the SEM battery

3.2.2.1 Cell Monitoring System

There is a large variety of battery monitoring chips that are used to measure the voltageand temperature of the different cells in a battery pack. Linear Technology, AnalogueDevices and Texas Instruments all provide possible solutions and will be discussed below.

AD7280A [24] is a modular battery monitoring system from Analog Devices. It canmonitor up to six cells per chip, but multiple AD7280A’s can be stacked on top of eachother through its daisy-chain interface to accommodate up to 48 cells connected in series.The AD7280A supports cell balancing, temperature and voltage measurements. It usesthe Serial Peripheral Interface (SPI) communication protocol to communicate with themaster controller.

LTC6802-1 [25] is a modular battery monitoring system from Linear technologies. Itcan monitor up to 12 cells per chip, but multiple LTC6802-1 can be stacked on top ofanother via its daisy-chain interface to accommodate a high battery voltage (>1000V).The LTC6802-1 supports cell balancing, temperature and voltage measurements. It alsouses the SPI communication protocol to communicate with the master controller and itis equipped with an open wire connection fault detection function.

BQ76940 [26] is a centralized battery monitoring system from Texas Instruments (TI).The chip periodically measures the cell voltage, battery current and battery temperature.It supports up to 15 cells and 3 temperature sensors. Multiple BQ76940’s cannot bestacked on top of each other to accommodate an increased battery voltage. It aims tobe a complete battery monitoring solution in itself. The BQ76940 has a current sensoras well as an output for a FET solid state switch. The solid state switch can be used tocontrol the output of the battery. It uses the Inter-integrated Circuit (I2C) communicationprotocol to communicate with the master controller.

Another important feature that is included within the BQ76940, is a built-in extralayer of analogue protection. As soon as one or more of the sensors sense a value out-side of the specified operating range, the solid state switch opens and thus protects thebattery. The maximum overvoltage (OV), undervoltage (UV), short-circuit (SC) andovercurrent values are programmed into the BQ76940 directly after the system start-up.



The periodical measurements are continuously monitored and the solid state switch iscontrolled independently, thus not by the MCU but by the BQ76940 itself. This protectsthe battery if the digital control circuitry from the MCU should fail for some reason.

The BQ76940 was chosen as the battery monitoring chip for this project due toits ability to measure all the required variables, and also due the additional protectioncircuitry that it provides. The BMS needs to be operational over extended periods ofoperation. If the MCU should fail for some reason, the BQ76940 chip will keep monitoringthe voltage of all the cells and the current of the battery. If some fault occur the switchis opened to protect the battery from damage.

3.2.2.2 Cell Balancing

The BQ76940’s cell balancing feature is implemented within the proof of concept BMSdesign. Each cell is equipped with its own balancing circuit. All of the different cellbalancing circuits are situated on the proof of concept BMS printed circuit board (PCB)along with other components such as the BQ76940 and the MCU. Each cell balancingcircuit is controlled with a P-channel MOSFET which is switched on or off using theBQ76940 chip. The MCU controls which cells are balanced by programming the BQ76940chip.

A 20Ω balance resistor was selected to discharge the cell being balanced. This resultsin a balancing current of 200mA which is 3% of the LP55100100 cell capacity (Ah).Balancing currents of 1% of the battery capacity (Ah) are adequate for applications thatstay in standby for long periods, e.g. electric vehicles, according to [27]. The balancingcurrent is chosen in such a way that the proof of concept BMS is compatible with batterypacks with a larger capacity than that of the SEM battery pack.

3.2.2.3 Current Sensor

For the safety of the battery and the application it is extremely important to regularlymeasure the battery current to avoid continuous overcurrent conditions. The BQ76940chip is equipped with a 16-bit integrating analogue-to-digital converter (ADC) that pro-vides measurements of accumulated charge across a current sense resistor. This func-tionality is used since it reduces the overall system complexity and it also provides anaccurate measurement of the battery current.

Shunt resistors used for current sensing purposes are ideal for smaller battery packsdue to its low cost and simplicity. Larger battery packs, typically, do not use shuntresistors, because of the high power losses and the effect of temperature on the resistor’svalue. The value of the shunt resistor used in this design is chosen as 2mΩ. The valueof the resistor was chosen as such to minimise losses, since efficiency is of great concernfor this design. This does however slightly lower the measurement accuracy.

3.2.2.4 Solid State Switch

The BQ76940 chip provides two low side FET drivers. These may be used to controlback-to-back power FETs, which acts as a solid state switch. Two FETs are used, one tocontrol each direction of the battery current. This has the advantage for example thatif the battery is fully charged, the charge FET can be opened to only allow a discharge



current. This functionality does result in higher losses in the switch, but are rarely used.It is only used on the edge of operation, meaning at very low or high SOC. The FETschosen for the proof of concept BMS was the FS3207 power metal-oxide-semiconductorfield-effect transistor (MOSFET) from International Rectifier. It is chosen for its lowon-state resistance of 4.5mΩ and minimum breakdown voltage of 75V.

3.2.2.5 Micro Controlling Unit

The MCU used in this design is the Piccolo TMS320F28035 [28] from TI. TI MCUs arechosen for all the electronic development done within the electrical drive train. Thisensures compatibility and eases the integration process between the different workingparts of the overall project.

The TMS320F28035 was chosen for its Controller Area Network (CAN) functionality.It is the smallest available TI MCU with CAN functionality. The CAN interface will beused, at a later stage, to communicate with the dashboard unit of the prototype EV.

The MCU is used for the processing of data and receives the battery data via theI2C communication protocol. The MCU controls the complete BMS apart from partiallycontrolling the solid state switch. The MCU processes the data it received from themonitoring system such as the cell voltages, battery current and pack temperature. It isalso in charge of cell balancing and can control the output 48V battery supply via theBQ76940 chip.

3.2.2.6 Low Power Buck Regulator

A low power regulator is required to step down the battery voltage to power the MCUand future communication interfaces. The supply voltage is required to step down from48V to 5V and 3.3V. The battery voltage can fluctuate, depending on its state of charge,between a minimum voltage of 41.6V and a maximum voltage of 54.6V.

The LM5017 [29] buck regulator from TI is selected for this purpose. It can delivera maximum current of up to 600mA which is adequate since the MCU uses a maximumconstant current of 200mA. The LM5017 is primarily chosen for its 600mA currentsupplying capability, since additional current will be required at a later stage for theimplementation of the communication interfaces.

Secondly, the LM5017 is chosen for its flexibility in terms of its wide input voltagerange, adjustable switching frequency and under voltage lockout protection. It is alsoequipped with other protection features such as thermal shut down and a peak currentlimiting circuit which protects against overload conditions. This makes the LM5017 idealfor the purpose of this project.

The design to step down the battery voltage to 5V through the LM5017 is shown inAppendix A.1. The 5V supply will also be used in future versions to power the isolatedCAN transceiver. Isolated CAN is required since the solid state switch is connected to thenegative terminal of the battery. The 5V is stepped down to 3.3V using the TPS73633which is a linear regulator from TI. The 3.3V is used to power the MCU.

The voltage ripple on the 3.3V rail is shown in Figure 3.3. The switching frequencyof the LM5017 is 237.3 kHz which confirms the designed switching frequency detailed inAppendix A.1. The ripple voltage is 58.5mV. The large ripple voltage can be attributed



−0.000010 −0.000005 0.000000 0.000005 0.000010Time [s]

−0.02

−0.01

0.00

0.01

0.02

0.03

0.04

0.05

Volta

ge[V

]Ripple

Figure 3.3: Voltage ripple

to the fact that a ripple voltage of 25mV is required at the feedback pin to enable theLM5017 to react.

3.2.2.7 Temperature Sensor

The temperature of the battery pack is measured with three thermistors. The 103AT-2of Semitec is selected according to the BQ76940 datasheet. It specifies the use of 10 kΩnegative temperature coefficient (NTC) thermistors for measurements.

3.3 Full Scale Battery Management System

3.3.1 Introduction

The proof of concept BMS detailed in the previous section is scaled up for the purposeof a micro electric vehicle in this section. The required battery capacity is 10 kWh with amaximum continuous power output of 5 kW. The battery terminal voltage is selected as48V since this reduces the risk of fatal electric shocks. The required power specificationresults in a nominal current of 104A. By scaling the discussed BMS to meet all of thespecification for the EV, new design challenges are introduced. The design choices madeto address these challenges are discussed in this section.

3.3.2 System Overview

The full scale BMS is based on the proof of concept BMS that is expanded into multiplecomponents. The complete proof of concept BMS was manufactured onto a single PCB.



Due to the scale of the micro EV battery, the full scale design is forced to be divided intoseparate PCBs, which include:

1. A main control board, which measures the different battery attributes and interpretsthem.

2. A current sensor board to deliver a voltage representation of the battery currentthat can be measured by the main control board. Currents of this magnitude couldpossibly induce Electromagnetic Interference (EMI) onto the cell voltage measure-ments if the sensor remained on the main board as with the proof of concept design.

3. Separate balancing boards are required and mounted on each parallel set of cells toincrease the battery balancing current.

The other circuit components within the system that was not developed include amechanical contactor and a fuse that serves as a back up to the contactor if it shouldfail. A condensed version of the complete BMS is shown in Figure 3.4. Starting fromthe left: the system has a local buck regulator to supply power to the MCU and variouscommunication hardware (CAN, USB and Bluetooth). The MCU is the master controllerand controls the 48V battery output by using a mechanical contactor. It also monitorsthe battery current through the current sensor board and does cell balancing duringcharging. The voltage and temperature measurements are done with a battery monitoringchip (BQ76940). Custom balancing PCBs are designed for cell balancing purposes inorder to enable high power cell balancing. The battery cells are connected in a matrixconfiguration to minimise the total required cell voltage measurements. The full scaleBMS installed along with the battery pack can be seen in Figure 3.5. The design choicesmade are discussed in detail in the following subsections.

B1

B2

B15

Monitorchip

LowPower

DC-DCI2CMicro-

controllerUnit

3.3 V

Communication250 A

F1 CurrentsensorContactor

+

48 V

M1

N1 −

Figure 3.4: Full scale BMS circuit diagram



Figure 3.5: Full scale battery pack

3.3.3 Battery Cell Configuration

The cells used inside the battery pack are 100Ah LFP (LY-100AH) cells from LiyuanBattery Corporation. The battery chemistry is chosen for its high cycle life and highsafety. The LFP cells have a nominal voltage of 3.2V each, resulting in nominal batteryvoltage of 48V when 15 of the cells are connected in series. The specifications of thecells are shown in Table 3.2. Two sets, of 15 cells connected in series, are used to achievethe required capacity of around 10 kWh. The resulting battery pack is a 48V - 200Ahbattery pack, which has a capacity of 9.6 kWh.

The two strings are connected in the matrix configuration, i.e. two cells are connectedin parallel, with 15 of these parallel connections connected in series. This configuration isselected to minimise the required cell voltage measurements. Only 15 parallel connectedcell voltages need to be measured. Another advantage, with careful selection, is that thecapacity tolerances of the cells can be used to cancel each other out by connecting a lowcapacity cell in parallel with a higher capacity cell. This will typically entail testing eachindividual cell’s capacity before constructing the battery pack.

A digital model of the full scale BMS installed along with the battery pack is shown

Table 3.2: LY-100AH cell specifications

Parameter RatingNominal voltage 3.2VMaximum voltage 3.6VMinimum voltage 2.5VNominal capacity 100Ah at 33A

Normal discharge current 33ANormal charge current 33A

Maximum discharge current 500AMaximum charge current 300A



Figure 3.6: Digital model of the battery pack

in Figure 3.6. The battery pack is divided into an upper and lower level due to sizeconstraints. All the control circuitry is packaged close together within the remainingspace allotted to the battery pack. This simplifies the installation of the BMS onto thebattery. It also results into a battery pack with a symmetric form as specified by the EVmanufacturer.

3.3.4 Fuse

The central fuse of the system is added to protect the battery pack from overcurrentconditions, either during charging or discharging. The fuse acts as the last resort currentprotection in situations where for example there is a short-circuit current, or the MCUor current sensor should fail for some reason.

The fuse selected is a 250A fast acting fuse. The battery pack can deliver up to600A and receive up to 300A without being damaged according to the datasheet. Themaximum continuous power that the system requires to deliver is 5 kW, which results ina maximum current of 104A. The maximum peak power is limited by the current sensor,since the current sensor is only rated to measure currents up to 150A. This is well belowthe 250A rating of the fuse.

3.3.5 Main Control Board

The main control board is responsible firstly to measure all the battery attributes and sec-ondly to regulate the battery output. The components used within the previous designedBMS that are also used within the full scale design include the MCU (TMS320F28035)and battery monitoring chip (BQ79640). The manufactured main control PCB is shownin Figure 3.7 and the schematic can be seen in Appendix C.1.1. Reusing as much aspossible of the previous design reduced the development time since many of the firmwarehas already been developed.

Similarly as the proof of concept BMS, the full scale BMS is centered around theBQ76940 battery monitoring chip from TI. Again the core functionality of the chip is



Figure 3.7: Main control BMS PCB

to monitor the 15 cells connected in series as well as to provide the additional analogueprotection circuitry. The BQ79640 is able to control the mechanical contactor using anAND gate and the cell voltage measurements. The current measurement is no longerperformed by the BQ79640 which results that the BQ79640’s analogue SC and overcur-rent protection are lost. The BQ79640’s analogue protection is active for OV and UVconditions.

Other changes that were implemented from the proof of concept BMS include theaddition of communication interfaces that can be used to communicate the data andstates of the battery to a driver interface. A different low power buck regulator is alsoused. These changes are discussed in the following subsections.

3.3.5.1 Bluetooth Communication

The LMX9839 [30] Bluetooth module from TI is added to the design to provide theBluetooth capability. This will typically be used for battery diagnostics and data logging.This module was chosen since it is a complete solution with a built-in antenna. Thishardware design is included but not tested in this project. The software development ofthe Bluetooth interface is outside of the scope of this project and will be added at a laterstage.

3.3.5.2 CAN Communication

The SN65HVD234 [31] transceiver from TI is added to the design for its CAN functional-ity. The CAN protocol delivers a robust solution for communication between the varioussystem components for example the BMS and the battery charger. It can however alsobe used for battery diagnostics and data logging. The SN65HVD234 transceiver actsas a buffer between the MCU and the CAN bus. The transceiver was chosen because itmeets the requirements of the system. This hardware design was succesfully implementedwithin this project. The software development of the CAN interface is discussed in detailin Chapter 4.



3.3.5.3 Powerline Communication/ USB Communication

Originally, the PLC-stamp mini from I2SE was also added to the design for its power-linecommunication functionality. The PLC-stamp mini is a small breakout board. Unfortu-nately, it only became apparent at a later stage that using this technology is currentlytoo expensive to implement. This will however change over time as the price of the tech-nology decreases. Therefore, it is kept in the design for development and testing at alater stage. The PCB layout is done in such a way that other breakout boards can alsobe used instead of the PLC-stamp mini.

USB communication was added via Future Technology Devices International’s (FTDI)FT220 [32] breakout board which uses the same layout as that of the PLC-stamp mini.This is the primary interface used to send data from the BMS to a computer. USB com-munication is easy to set up on the computer’s side. A small Python listener applicationis used to read the incoming battery data into a database.

3.3.5.4 Low Power Buck Regulator

A low power regulator is used to power the MCU as well as all of the different communi-cation interfaces. The supply voltage is required to be 3.3V. The LM5017 buck regulatorwas used in the proof of concept design. Unfortunately, it has a very low feedback preci-sion which results in a voltage ripple higher than desired. It is for this reason that for thefull scale BMS design, the TPS54060 [33] buck regulator from TI is chosen. The designis done using TI’s SwitcherPro software package.

The voltage ripple of the regulator is shown in Figure 3.8. The voltage ripple hasdecreased to 10mV. The lower ripple minimises the regulator’s influence on the voltageand current measurements. The switching frequency of the device is 143.7 kHz whichconfirms the design.

−0.000015 −0.000010 −0.000005 0.000000 0.000005 0.000010 0.000015Time [s]

−0.008

−0.006

−0.004

−0.002

0.000

0.002

0.004

Volta

ge[V

]

Ripple

Figure 3.8: TPS54060 voltage ripple



3.3.6 Balance Circuit Design

The full scale BMS, similar to the proof of concept BMS, deploys passive cell balancing tomaximise the battery capacity. In large scale systems the magnitude of the cell balancingcurrent is large, thus a lot of heat is dissipated. The heat is substantially more than whatcan be dissipated on the BMS’s main control board. For this reason a small balance PCBis designed, one for each set of parallel connected cells, to distribute the heat across alarge area during cell balancing. The circuit diagram of the PCB is shown in Figure 3.9and the PCB schematic can be seen in Appendix C.1.2.

The size of the balance current is typically chosen according to the cell capacityand tolerance. A minimum balancing current of 1A was selected for this project, aswas suggested by Julian Gerber, an engineer involved in the Joule EV project. He alsoconsulted the University of Stellenbosch during this project.

Balancing is activated by the battery monitoring chip that is situated on the maincontrol board. The chip’s ADC pins are connected internally to enable a P-channelMOSFET (M1) externally, which enables cell balancing of that specific cell. A fuse (F1)is used to protect each cell connection from a short-circuit fault. A transient voltagesuppressor (TVS) (Dt) and two diodes (D1 and D2) are used to protect the MOSFETfrom electrostatic discharge (ESD). It is also disconnects the output by blowing the fusein case the PCB is connected to the cell the wrong way around. The resistor Rf is partof the BQ79640 hardware set-up and is used in combination with a capacitor, to act as afilter. The resistor Rg is used as a gate resistor to limit the inrush current which enablesthe balancing MOSFET. The resistor Rt is a 10 kΩ NTC thermistor and is selected assuggested by the BQ76940 datasheet. The thermistor is situated close to the batteryterminal on the PCB. This ensures an accurate cell temperature measurement.

The PCB is designed to bolt onto the terminals of a cell. It was decided to use a PCBtrace as the balance resistor Rb. This saves on some of the cost of the system since nohigh power resistors need to be bought and it reduces the height of the balancing PCBswhich was a constraint in the physical design of the battery pack. The required tracewidth for a specific current and temperature increase can be approximated [34] by

I = 0.048 ·∆T 0.44 · A0.725, (3.3.1)

where

I = maximum current [A],

∆T = temperature rise [C] ,

+Vcell −

Rb

M1

Rf

Rg

D1

D2

+F1

Dt

Rt+-

Figure 3.9: Balance circuit diagram



A = cross-sectional area [mil2].

The minimum balancing current was specified as 1A. This results, with a temperaturerise of 10C, in a PCB trace width of 0.3mm.

The total balance resistance is chosen as 2.5Ω. The cells are typically balanced ata voltage of 3.6V, limiting the balancing current to a maximum of 1.44A. The internalresistance of the MOSFET is 0.14Ω, which results in a required trace resistance of 2.36Ω.The resistance of the trace can be calculated by

R = ρ · L

T ·W · (1 + α · (temperature− 25C)), (3.3.2)

where

ρ = resistivity,

L = length,

W = trace width,

T = trace height,

α = temperature coefficient.

The values of ρcopper and αcopper are 3.9 · 10−3 Ω·cm and 1.7 · 10−6 1C respectively.

The length of the trace is calculated as 1.4m with a trace thickness of 35µm. Thisresults in a balancing current with a minimum value of 1A and a maximum value 1.44A.During practical testing the balancing current was measured to be 1.25A. A manufacturedbalance PCB connected to a cell’s terminals is shown in Figure 3.10.

Figure 3.10: Balancing PCB connected to cell terminals

3.3.7 Current Sense PCB Design

Accurate current measurements are required to successfully monitor the battery pack.This section details the design of the high current measurement board. The currentmeasurement board is separated from the main control board for several reasons.

The board is designed to measure a maximum steady state current of up to 120A. Asafety margin is added since only 104A is required to provide an output power of 5 kW.To achieve such a high current rating the PCB trace width and trace thickness need tobe maximised.

Increasing the trace thickness increases the overall cost of the PCB dramatically.One of the manufacturing constraints is that a PCB typically only have one PCB trace



thickness unless it is a specialised PCB which is expensive. The current sensor is thereforesplit from the main control board to minimise the PCB area that requires extra thicknesstracks.

Splitting the design also reduces EMI on the main control board. The high motorcontroller switching current needs to be as far away from the main control board aspossible to minimise the coupled noise. This noise will typically reduce measurementaccuracy.

3.3.7.1 Current Sensor Selection

There are two types of sensors that can be used to measure the current of the battery.The first is a shunt resistor, as was used in the proof of concept BMS design. The secondis the use of a Hall effect-based current sensor.

The shunt resistor method has some disadvantages when it comes to high currents.In extreme fault conditions the conductor connecting the battery and the shunt resistorcan induce voltage spikes which can damage the measuring device. This is due to thechange in battery current with respect to time ( δi

δt). The corresponding generated voltage

is calculated by: V=L δiδt. All conductors have some equivalent series inductance (L)

even though it is very small. This phenomenon decreases the overall robustness of thissolution.

Another drawback with using a shunt resistor, is the power dissipated within theresistor. It is important that adequate resistance is chosen. If the resistance is too large,too much power is dissipated which decreases the overall efficiency of the circuit. On theother hand if the resistance is too small, the accuracy of the measurement is decreased.The value of the shunt resistor is typically chosen extremely small to minimise the amountof power dissipated within the resistor. A shunt resistor with a small tolerance andtemperature coefficient is usually quite expensive. An operational amplifier is used toamplify the shunt resistor’s voltage measurement in order to increase the resolution ofthe measurement. This also adds to the complexity of the system.

A Hall effect current sensor is another option. It operates on the principle of using themagnetic field generated by the current to measure the size and direction of the current.The ACS758KCB-150B-PFF [35] sensor from Allegro is chosen for this design. The Halleffect sensor is much more suitable for high current applications and provides superiorperformance to that of the shunt resistor. The sensor chosen has a series resistance of100µΩ which is much smaller than that of a corresponding shunt resistor required for thisapplication. The Hall effect sensor provides galvanic isolation which isolates the sensor’soutput from voltage spikes and it allows high-side voltage current sensing. No additionalamplification of the measurement is required and as such it provides the complete currentmeasuring solution. The price of the Hall effect sensor also compares very well to that ofa high quality shunt resistor.

3.3.7.2 Sensor Design

A simplified diagram of the current sensing circuit is shown in Figure 3.11. The thickerlines represent high current paths. A low pass filter Rf and Cf , is shown at the outputof the current sensor. This is used for noise filtering.



I in

I out

RfCf

CS out

Rp

PC out

D13.3 V

RPC in S1

Currentsensor

SSC

Figure 3.11: Current sensor circuit diagram

A pre-charge resistor Rp is also added to the current sensor design. The batteryis typically connected to loads that have some form of bus capacitance. Initially themechanical contactor is connected in open-circuit to allow the bus capacitance to chargeat a controlled rate in order to prevent high inrush currents that can harm the battery,the mechanical contactor or the load itself. A pre-charge resistor is used for this purpose.The mechanical contactor’s one terminal is connected to the output terminal (I out) ofthe current sense PCB. The pre-charge output (PC out) is connected to the mechanicalcontactor’s second terminal. A small solid state contactor (SSC) is used to control theoutput of the pre-charge circuit to the external bus capacitance. Once the voltage acrossthe capacitors equal that of the battery potential, the main mechanical contactor is closed.

The value of the pre-charge resistor Rp was chosen according to the motor controller’sdatasheet information as 470Ω. The SSC was chosen according to the pre-charge resistor’svalue and the maximum voltage of the battery which is 54.75V. A transient voltagesuppressor (TVS) D1 is used to protect the SSC from electro static discharge (ESD) aswell as from fault conditions where the voltage across the mechanical contactor couldspike. The SSC is controlled by the MCU by means of a MOSFET S1 and resistor R.The PCB schematic can be seen in Appendix C.1.3. The manufactured current sensePCB is shown in Figure 3.12.

3.3.7.3 Thermal Design

Accommodating a current of 120A on a PCB is a challenge. For this design a two layerboard was selected to minimise the cost of the PCB. The PCB layer thickness was chosenas 140µm on each side. This is four times the thickness of the standard of 35µm. Thetop and bottom layers are stitched together with via’s to ensure both layers are usedeffectively. This results in a combined layer thickness of 280µm. The required tracewidth is calculated using (3.3.1) and (3.3.2). The trace width is calculated using theworst case scenario in mind: an ambient temperature of 40 C, a current of 120A and a



Figure 3.12: Current sensor

temperature rise of 15 C. The resulting trace width is 22mm. The thermal results willbe discussed in more detail in Chapter 6 as part of the results discussion.

3.4 Prototype Solid State Contactor

3.4.1 Introduction

A SSC is designed, in this section, as a possible replacement for the mechanical contactor.Possible advantages a solid state contactor has over a mechanical contactor are a fasterreaction time, less wear as well as being more energy efficient.

The main focus of this design is on ensuring the SSC is more energy efficient thanit’s mechanical counter part. It is for this reason that the design is centered around thespecifications of the mechanical contactor. The basic operation of a mechanical contactoris therefore discussed in the following subsection. The rest of the subsections discuss thedesign of the SSC.

3.4.2 Mechanical Contactor

The mechanical contactor shown in Figure 3.4 is used as a switch to connect or disconnectthe battery from the load. In this subsection a mechanical contactor is investigated andis used to determine the different specifications required for a SSC.

Mechanical contactors use an electrical current as input, to generate a magnetic fieldin the control coil. This magnetic field closes the mechanical contact. Typically, themagnetisation current is the most energy inefficient part of the mechanical contactor.In order for the contactor to remain closed, magnetisation current is required. Thus,as long as the contactor is closed, power is being dissipated. The mechanical contactoralso dissipates power during use due to the contact resistance of the pole. This contactresistance typically increases with use due to the build-up of compounds on the contactswhich is caused by arcs when the contactor disconnects.

The mechanical contactor used in this project is the SW80B [36] from Albright In-ternational. It is a 100A rated single pole, normally open contactor. The continuouslyrated coil (on-state) power dissipation is between 7 – 13W according to the datasheet.



The contactor was tested for the intended application under normal operating conditionsto more accurately determine the coil’s power dissipation. A coil dissipation of 11W wasmeasured. This value stays constant while the contactor is closed, irrespective of the loadpower usage.

The datasheet also specifies that the typical voltage drop per pole across new contactsat 100A, is 40mV (4W). Interpolated for 104A, this would result in a loss of 4.326W.The total power dissipation in the mechanical contactor at a current of 104A is thus15.326W. The SSC must thus dissipate less power in order to be more energy efficient.

3.4.3 Solid State Contactor Design

When designing a SSC, all the solid state switching technologies need to be investigated.The most commonly used solutions currently include a MOSFET or an insulated-gatebipolar transistor (IGBT). For this project’s application, the contactor is used at highcurrent and low voltage. This makes the use of a MOSFET an appropriate solution, sincelow voltage MOSFETs have very low on resistances. Thus, MOSFETs were chosen overother technologies such as IGBTs.

3.4.3.1 Design Overview

MOSFETs can only control current in one direction because of its freewheeling diode.Therefore, an extra MOSFET is added to ensure current control in both directions. Thisresults in two MOSFETs in series, connected back to back with a common source. TheMOSFETs need to be powered by an isolated supply to ensure operation within noisyenvironments. The solid state contactor must thus be isolated from the control circuitry.Opticalcouplers are used to deliver isolated signals to the gate drivers of the MOSFETs.MOSFETs do add some difficulties in terms of robustness.

3.4.3.2 MOSFET Considerations

It is critical that the SSC is robust and disconnects the battery from the load when desiredunder all circumstances. The worst case scenario is a short circuit condition across thebattery terminals. If the SSC is unable to disconnect the battery from the load in theevent of a short circuit condition, it could lead to the battery failing. A Li-ion batteryfailing, could lead to a fire or even an explosion.

MOSFETs are susceptible to being damaged in the case of overvoltage or overcurrentconditions. In the worst case scenario, the Miller capacitance can also lead to the MOS-FET being operated within the linear region. This would cause the MOSFET to dissipatetoo much power, which in turn could lead to MOSFET’s failure. MOSFETs also needshort circuit protection and thermal runaway protection to protect it from damage.

3.4.3.3 Detailed Design

MOSFET: A simplified schematic of the SSC design is shown in Figure 3.13. The de-sign is based on ultra-low resistance MOSFETs. The MOSFETs used is the CSD19535KTTPower MOSFET from TI as is shown in Table 3.3. To further reduce the internal resis-tance of the SSC, four MOSFETs were connected in parallel, which reduced the overall



Table 3.3: CSD19535KTT Power MOSFET specifications

Parameter RatingVDS maximum voltage 100VVGS maximum voltage ± 20V

RDS(on) 2.8 mΩ at 10VID maximum drain current 197A at 25 CQg maximum gate charge 98 nC at 10V

Junction-to-case thermal resistance 0.5 C/W

resistance to 0.7mΩ. This action was required to ensure the SSC is comparable to themechanical contactor in terms of efficiency. Two sets of parallel MOSFETs are connectedin series, back to back. This configuration minimises the required amount of DC-DCconverters to one, which reduces the overall cost of the system.

The two series sets allow the control of the battery current in both directions. Thisincreases the overall resistance to 1.4mΩ. A current of 104A results in a power dissipationof 15.14W, which is within the design specification.

It is important to note that the power dissipated inside the coil of the mechanicalcontactor, when it is within its closed state, is responsible for the majority of the powerbeing dissipated. This results in a very inefficient switch when only a small amountof power is being delivered to the load. The on-state power dissipation of the SSCon the other hand is very small. It is negligible compared to the series resistance powerdissipation. This is confirmed in the results section. Even though at maximum continuouspower the two contactor’s efficiencies are roughly equal, at low power usage the SSC’sefficiency declines quadratically with the load current.

Isolated Converter: The MOSFETs are controlled by MOSFET drivers which arepowered by an isolated DC-DC converter. This isolates the MOSFETs from any voltagespikes on the high current carrying conductors. It also allows the SSC to be connectedat the positive terminal as was the case with the mechanical contactor.

The supply voltage of the MOSFET driver was chosen as 12V. It is the midway voltagebetween the driver’s maximum voltage and the minimum on-voltage of the MOSFETs.The chosen DC-DC converter for this project is the MEU1S1212ZC [37] from MurataPower Solutions. Both the input and output voltages are rated to 12V. The converterwas chosen for its small size, cost and voltage rating.

Optical Isolator: The input signals to the SSC are also required to be isolated in orderto isolate the control of the SSC from the main control board. This can easily be achievedby means of optocouplers. The 4N35 [38] from Fairchild is selected for this project. Itdelivers a good trade-off between performance and cost.

Transient Voltage Suppressors: One of the problems with MOSFETs are its sensi-tivity to overvoltage. It is for this reason that transient voltage suppressors (TVS) are



High Current input

GND-ISO

High Current output

RG1 RG2 RG3 RG4

RG5 RG6 RG7 RG8

Mosfetdriver 1

12 V ISO

OUT1

R1CS

R2

GND-ISO

C1 D1

Mosfetdriver 2

12 V ISO

OUT2

R3CS

R4

GND-ISO

C2

D2

R5IN2

3.3 V ISOINPUT 2

R11

R6

IlimR7

D3

IN1R8

GND-ISO

3.3 V ISO

R9

IlimR10

D4

INPUT 1

R12

ISOSupply

12 V 12 VD5

Figure 3.13: Solid state contactor circuit diagram

added to the design. When a large current is disconnected by the SSC, the inductancewithin the conductors causes a voltage spike on the SSC terminals. The TVS clampsthe voltage at a set value. The TVS chosen for this project is the 5KP54CA-TP [39]from Micro Commercial Co. It can dissipate peak power pulses of up 5 kW at a maxi-mum clamping voltage 87.1V. The peak pulse current is 57A. Three of these TVS areconnected in parallel to accommodate the worst case battery current scenario of 120A.

MOSFET Driver: Each set of parallel MOSFETs are controlled by a MOSFET driver.The UCD7100 [40] from TI is selected for this purpose. Another MOSFET driver thatwas investigated is the TD352 from ST. The UCD7100 was chosen for its high sink andsource current of 4A. A large current is demanded in order to switch all of the parallelconnected MOSFETs on at the same time.

Another functionality that is crucial for this application is the current limit protection.With this functionality, the driver can turn off the power stage in the unlikely event thatthe digital system cannot respond to a failure situation in time. The UCD7100 has twoinputs to achieve this: a current sense (CS) pin and a current limit (Ilim) pin. Accordingto the datasheet these two inputs are compared and when the CS voltage level is greaterthan that of the ILIM voltage minus 25mV, the output of the driver is forced low. Areference voltage can be given to the Ilim pin to change the overcurrent threshold voltageof the MOSFET driver.

This current sense functionality is used to solve two possible problems: thermal run-away and short circuit conditions. The idea is to sense the voltage across the set ofMOSFETs while they are switched on. The voltage across a MOSFET can only increasedue to one of two reasons. Either the current increased through the MOSFET, or theMOSFET’s internal resistance increased due to a temperature rise. A dead-time circuit



is designed to delay voltage feedback from the CS pin as shown in Figure 3.14. Thisensures that the current limit protection is not activated during the time period requiredfor the MOSFETs to switch on.

R1

CS

R2

OUT

C1

D1

RDS(ON)

Figure 3.14: Dead time design

In Figure 3.14, OUT is the power output of the MOSFET driver, CS is the currentsense pin of the driver and RDS(ON) is the internal resistance when the MOSFET is turnedon. Before the MOSFET is switched on the diode D1 is typically reverse biased becauseof the high voltage across the SSC terminals. The diode is only forward biased whenthe MOSFET is switched on. Resistor R2 is used to limit the current flowing throughD1 during operation. Resistor R1 and capacitor C1 are added to introduce dead-timedead-time to the CS pin. This allows some time delay for the MOSFETs to switch onbefore normal operation of the UCD700 continues.

The derivation of the dead-time’s mathematical equation are done within the fre-quency domain. The assumption is made that the diode is reversed biased. A step inputat the OUT pin of the drive results in a voltage

VCS =

(1

C1R2

s+ R1+R2

C1R1R2

)(VOUTs

), (3.4.1)

at the CS pin. Calculating the inverse Laplace transform of (3.4.1) yields the time domainresponse

VCS =

(R1

R1 +R2

)(1− e−

R1+R2C1R1R2

t)VOUT . (3.4.2)

at the CS pin.A dead-time below 1µs is chosen. This minimises the risk of the SSC failing. The

increase in current, within the dead-time delay, is limited by the inductance of the con-ductors.

A check is required to ensure the MOSFETs turn on before the dead-time has passed.The MOSFET’s input gate pin is approximated by their total gate capacitance. The gatecapacitance can be estimated by Cg = Qg

Vgas 9.8 nF. The gate resistor, through which the

MOSFETs are controlled, is chosen as 15Ω. This yields a simple RC circuit. The timedomain function of the input gate pin can be approximated by

Vgate = VOUT (1− e −tRC ). (3.4.3)



According to the datasheet, the MOSFET is said to be fully switched on when Vgate =8V . The switch time for the gate voltage to reach 8V, is 161.5 ns. Adding the MOSFET’sturn-on delay of 33 ns yields a total time delay of 194.5 ns. This results in a large safetyfactor between the time required for the MOSFETs to be fully switched on and beforethe dead-time has passed.

Once the MOSFETs are switched on, the voltage present at the CS pin is the forwardbias voltage of the diode plus the voltage across the MOSFET. In order to enable bettercontrol of the output current of the gate driver, only the voltage across the MOSFET’sdrain and source terminals are required. This voltage is typically very small. The problemis that the diode voltage differs with current and temperature. It is for this reason that asimilar diode, D4, is placed in series with the reference resistors to set the current limit.When similar magnitude currents flows through both diodes, the two voltages cancel out.The manufactured SSC is shown in Figure 3.15.

3.4.3.4 Thermal Design

Ensuring that the SSC operate at the optimal temperature is crucial to making it morerobust. The thermal design of the SSC is discussed in the following section.

Power dissipation: The thermal design is conducted for a battery current of 120A.This adds an extra safety factor since the maximum continuous rated current is 104A.There are four MOSFETs in parallel and the assumption is made that the current aredivided equally between them. This results in a current of 30A per 2.8 mΩ MOSFET.The maximum power dissipated per MOSFET is 2.52 W. The switching losses are ignored,since the SSC is not design for high frequency switching.

Design: The maximum ambient temperature is selected as 40C. The maximum junc-tion temperature is chosen not to exceed 70C which results in a temperature rise of

Figure 3.15: Manufactured solid state contactor



30C. Therefore, the overall thermal resistance per MOSFET required for this design is11.9C/W. The MOSFET junction-to-case thermal resistance is 0.5C/W. The 7109DGfrom AAVID Thermalloy is a surface mount heat-sink with a thermal resistance of11C/W. It is chosen for this project for its small footprint. This results in a overallthermal resistance of 11.5C/W, which is within the required specifications. A heat-sinkis connected to every MOSFET on the PCB to dissipated the heat. The thermal resultswill be discussed in more detail in Chapter 6 as part of the results discussion.

3.5 ConclusionIn this chapter a proof of concept BMS was designed. The required design changes tothe proof of concept BMS were also implemented in order to scale the BMS for theapplication of a micro EV. This included splitting the designed circuitry into three parts,a main control circuit, balancing circuits and a current sensor circuit. This approachdelivered an adequate BMS in terms of cost and performance. A proof of concept designof a SSC was also investigated, which could possibly replace the mechanical contactor infuture iterations of the BMS.


Chapter 4

Software Design

4.1 IntroductionThe BMS is digitally controlled by means of a MCU. This ensures that the system isflexible and that it can be used with a wide range of different battery technologies withoutmaking hardware adjustments to the control circuitry. Only software changes have to beimplemented in order for the BMS to comply with different battery cells. The softwaredesign of the BMS is discussed within this chapter.

4.2 OverviewAn overview of the software design is discussed in this section. The MCU used for thisproject is the Piccolo TMS320F28035. The IDE used to program the MCU is Code Com-poser Studio from TI. The MCU is required to interact with a variety of components forthe BMS to function properly. The MCU controls the battery monitoring chip with theInter-Integrated Circuit (I2C) communications protocol. Battery data is communicatedvia Serial Peripheral Interface (SPI) to a Universal Serial Bus (USB) breakout boardwhich communicates with a computer. The battery data can also be requested via theCAN communication protocol. The CAN interrupts are used to respond to a measure-ment request. The analogue battery current is measured with an analogue-to-digitalconverter (ADC). Other peripherals used include Central Processing Unit (CPU) timersto gauge the time between samples taken.

The software design can be split into multiple sections and contains a main loop withan additional set of interrupts. The layout of the design is described in detail below.

1. The main loop is used to measure the cell voltages and battery temperature usingthe BQ79640 chip and thus forms the basis of the design. It sends the batterymeasurements via USB to a computer. Battery balancing is also performed withinthe main loop.

2. A current sensing loop is used to convert the current sensor’s analogue value intoa digital representation using the MCU’s ADC. The conversion triggers an ADCinterrupt after which the current sensing loop is executed.

47


CHAPTER 4. SOFTWARE DESIGN 48

3. A master control loop, which is situated within a CPU timer interrupt, is used tocontrol the logic of the battery. The logic is controlled via flags that are set withinthe main loop and current sensing loop. These flags convey the status of the batteryat that specific time.

The design of the different software loops are discussed in the following sections.

4.3 Main loopA simplified flow diagram of the main loop is shown in Figure 4.1. The flow diagramis shown to have an initialisation phase. Within this phase all the different peripher-als are set up. It includes the set-up of the ADC, CPU timers, SPI, I2C and CAN.The BQ79640’s analogue protection and initialisation are also programmed within thisinitialisation phase.

Start

Initialise:BQ79640

CPU TimersI2CSPI

ADCCAN

Timer0interrupt

T

Read BQ79640measurements

CRC match

T

F

Compare toconstraints

T

Balance cells

Send datato USB

F Set flagsOpen contactor

F

Figure 4.1: Main flow diagram

An if statement in combination with the CPU timer 0 is used to execute the mainloop only once every second. Within this loop the battery measurements are read via theI2C communication protocol from the BQ79640. These measurements include the cellvoltage measurements and the battery temperature measurements. The data received isput through a cyclic redundancy check (CRC) method to ensure the validity of the data.This ensures a more robust system in a typically noisy environment.



The measurements taken are then compared to the operating specifications as givenby the manufacturer. When the battery is operated outside of the specified operatingrange, the appropriate error status flags are set. The contactor is also opened in the caseof a fault condition.

Once the measurements are taken, the balancing method is called to balance thebattery pack. The balancing function is only executed if the status flags indicate balancingis required. All the battery measurements are sent to the computer by the USB FT220breakout board. On the computer’s side, a python script is used to process the data andstore it in a database. This script is shown in appendix B.1.

4.4 Battery BalancingThe balancing algorithm is shown in Figure 4.2 and is located in the balancing functionwithin the main loop. The operation of the function can be described as follows: thebattery is charged with the traditional constant current - constant voltage (CC-CV)method.

As soon as one of the measured cell voltage is higher than 3.65V, charging is stoppedand balancing is started. All cells with a measured voltage of higher than 3.45V isbalanced through the balancing circuitry. Balancing is terminated for a particular cellonce the cell’s voltage drops below 3.45V. The cell balancing action is thus terminatedonce all the cell voltages are below the reference voltage. This algorithm was suggestedby James Verster, whom is the CEO of BlueNova, a local energy storage company.

This is not the general balancing algorithm used for Li-ion cells. Typically, cellsare balanced during charging. The reasons for not using the general algorithm are theapplication of the battery and the battery chemistry used in this project. The applicationof the battery allows the battery to be charged and balanced during nights when it it notused. The battery chemistry exhibits extreme non-linear behaviour at a high and lowSOC. This results in a large cell voltage difference between cells that have a small SOCdifference, when the battery pack is nearly fully charged. In most cases this results inthe battery not being charging in the CV mode for long enough periods of time in orderfor the charging current to decrease significantly. This results in the balancing currentbeing negligibly small compared to the charging current. Therefore, balancing is onlyactivated once charging has stopped. Another characteristic of the LFP cells is that theircell voltage do not stay constant at the charging voltage after being fully charged, insteadthe cell voltages settle at approximately 3.4V. This characteristic is combined within thebalancing algorithm as a threshold voltage where balancing is terminated.

The battery pack is balanced using the BQ76940 battery monitoring chip. One of theconstraints of the chip is that adjacent cells cannot be balanced simultaneously. Balancingadjacent cells simultaneously can possibly damage the integrated circuit. The balancingalgorithm is therefore adjusted to ensure that this does not happen. The even numberedcells are balanced inversely with the uneven numbered cells. This ensures that adjacentcells cannot be balanced simultaneously. Each set of cells are balanced for a time durationof 5 seconds. This minimises the amount of balancing commands sent from the MCUto the BQ76940 chip. This algorithm does have a negative impact on the time requiredfor cell balancing, since adjacent cells requiring balancing causes the average balancing



current to be half of the maximum balancing current.The battery monitoring chip also impacts the average balancing current of the cells.

The total duty cycle devoted to cell balancing is approximately 70% of the 250ms period.This is due to a portion of the 250ms time duration is allocated to perform normal cellvoltage measurements using the BQ76940’s ADC. This ensures that the cell balancingdoes not affect the voltage measurements. It also ensures that the OV and UV protectionsdo not accidentally trigger, or that OV and UV conditions go undetected while cellbalancing is enabled.

Enterfunction

Initializevariables

Balancestatus

F

T

Counter==0

F

T

Resetbalancing

Balance unevencells above ref

Counter==period

F

T

Resetbalancing

Balance evencells above ref

Count ++

Counter >2 x period

F

T

All cellsbelow ref

F

T

Stopbalancing

Reset flagand counter

Stopbalancing

Exitfunction

Figure 4.2: Cell balancing flow diagram



4.5 Current sense loopThe current sensing flow diagram is shown in Figure 4.3. It shows the ADC interruptwhich is triggered by Timer1 on the MCU. This interrupt has the highest priority toensure it is not interrupted while a fault condition exists. The mechanical contactorallows for a slower reaction time. The sampling rate is therefore chosen as 2 kHz, sincethis will ensure the MCU reacts fast enough to a fault condition.

Enter ADCInterrupt

Read: ADCvalue

Convert ADCvalue

Compare toconstraints

T

First orderdigital filter

Acknowledgeinterrupt

Exit ADCInterrupt

F

Set flagOpen contactor

Figure 4.3: Current sensing flow diagram

Once the interrupt is received, the ADC value is converted from a measured voltage toa corresponding digital current value. The output of the battery is disconnected from theload by means of the mechanical contactor if the current measurement is outside of themanufacturer’s specified operating range. A flag is also set to notify the master controlloop of the status of the battery. The current measurement is passed through a firstorder digital filter in order to filter all the excess noise. The interrupt is acknowledged toenable the ADC interrupt to read in new values.

4.6 Master control loopThe master control loop monitors the battery flags in order to determine the battery’smode. The battery output is controlled accordingly. The flags are for current, temper-ature, charged and discharged indications. The current flag is set in the current sensingloop if the battery current falls outside of the specified current boundaries. The temper-ature flag is set within the main loop if the measured battery temperature falls outside ofthe specified operating range. The charged flag is set when the battery is fully charged,



when one of the cell voltages is higher than the maximum allowed voltage during charg-ing. The discharged flag is set when the battery is fully discharged, when one of the cellvoltages is lower than the minimum allowed voltage during discharging.

The master control loop, as shown in Figure 4.4, is executed within the CPU’s Timer1interrupt. This interrupt has a frequency of 100Hz. This allows the routine to quicklyrespond to the different external and internal inputs and interrupts.

There are three main modes that the battery can be in. In the normal mode all theflags are null and the charger is not connected. In combination with all these flags theBMS has some external inputs from the motor drive unit and the driver. These inputsare used to signal the BMS when to close the mechanical contactor if needed. One of theexternal inputs is the vehicle’s key switch used to signal the motor drive unit to start upand standby until the throttle is active. The key switch is also used to reset the currentflag after a fault condition.

Timer1Interrupt

Charger==0f_Discharge==0

F

T

Keyswitch==0

F

T

Keydrive==0

F

T

f_Current==0f_Temperature==0

F

T

Closecontactor

Opencontactor

Opencontactor

Setf_Current==0

Charger==1f_Charge==1

F

T

Charger==1

F

T

f_Temperature==0

F

T

Closecontactor

Opencontactor

Acknowledgeinterrupt

EndInterrupt

Figure 4.4: Master flow diagram



The second mode the BMS can enter is the charged mode. This is when the chargeflag is high and the battery is still connected to the charger. The charge flag is set whenone of the cell voltages is higher than the maximum allowed voltage during charging.This results in the contactor opening to stop charging. Typically, this is when balancingstarts. The charged flag is reset to null when all the cell voltages decreases below aspecific reference level.

The third mode is the charging mode. The BMS enters this mode during batterycharging. More specifically, the mode is entered when the charger is connected and thecharge flag is null. The mechanical contactor is closed in order for the battery to charge.

4.7 ConclusionIn this chapter, the software layout of the BMS was discussed. This included the varioussoftware design choices made. Flow diagrams were used to explain the flow of the software.The main loop is used to monitor the cell voltages and battery temperature and is alsoused for battery balancing. The current sensing loop is used for current measurements.Flags are used to notify the master control loop which then controls the SOC and currentof the battery.


Chapter 5

On-line Parameter Estimation

5.1 IntroductionThis chapter investigates the on-line parameter estimation of an equivalent circuit model(ECM) of a battery. An ECM proves a good trade-off between model accuracy and modelcomplexity. Unfortunately, the battery parameters tend to change over time as the healthof the battery deteriorates (irreversible physical and chemical degradation). This makesthe on-line parameter estimation a more realistic approach to energy management.

A wide range of methods exist to accomplish on-line parameter estimation. Typically,a set of current and voltage measurements are used as the input and output of a systemrespectively. By minimizing the the error between the measured output and estimatedoutput, the parameters of the battery can be estimated with reasonable accuracy. In [41]the recursive least squares (RLS) algorithm is used to estimate the battery parameters.The RLS algorithm is easy to implement on-line and is computationally efficient. It istherefore chosen to determine the parameters of the ECMs.

The following chapter include the mathematical derivations of the single polarisation(SP)- and dual polarization (DP) Thévenin ECMs such that the RLS algorithm can beapplied to the models. A simulation, in combination with the RLS algorithm, is used toprove the derived mathematics.

5.2 Battery model

5.2.1 Introduction

In order to use the RLS algorithm to estimate the parameters of an ECM, the mathematicsdescribing the model is required to be written in the correct form. The SP Théveninmodel is evaluated first as it is the most common ECM. This model is then later inthe chapter extended to the DP Thévenin model which characterises the battery moreaccurately. The derivation of the models to the suitable RLS algorithm format is shownin the following subsections.

54


CHAPTER 5. ON-LINE PARAMETER ESTIMATION 55

5.2.2 Single Polarisation Thévenin equivalent circuit model

The most commonly used ECM is the SP Thévenin model as shown in Figure 5.1. TheOpen Circuit (OC) voltage, VOC , is the equilibrium potential of the battery. For thepurpose of this thesis the OC voltage is assumed a function of the SOC of the battery.Also, the temperature is assumed to be constant. Thus, when the SOC decreases so willthe OC voltage. Typically this relationship is non-linear and will be determined off-line.The internal resistances include the ohmic resistance Rs and the polarization resistanceRt1. The equivalent capacitance Ct1 is used to describe the transient response.

−VOC+

Rs

Rt1

Ct1

Vt1+ −+

−

V (t)

I(t)

Figure 5.1: Single polarisation (SP) Thévenin model

The mathematical model is derived as

V (t) = VOC −RsI(t)− Vt1(t), (5.2.1)

Vt1(t) = Rt1I(t)−Rt1Ct1dVt1(t)

dt. (5.2.2)

The assumption is made that the open circuit voltage (VOC) remains constant overone sampling period [42]. By differentiating (5.2.1) with respect to time, a third equationis established. Solving these equations simultaneously, Vt1(t) can be eliminated in (5.2.1).The output V (t) is expressed in terms of I(t), dI(t)

dtand dV (t)

dtas shown in (5.2.3).

V (t) = VOC − [Rs +Rt1]I(t)−RsRt1Ct1dI(t)

dt−Rt1Ct1

dV (t)

dt(5.2.3)

This gives the same result as that in [43]. In the next section the more complex DPThévenin ECM is derived.

5.2.3 DP Thévenin equivalent circuit model

The DP Thévenin model is shown in Figure 5.2. This model adds an additional RCpair to more accurately match the dynamic behaviour of the battery voltage. The re-sistor Rt1 characterises the effective resistance of the electrochemical polarisation andRt2 characterises the effective resistance of the concentration polarisation of the battery.The capacitor Ct1 represents the electrochemical polarisation, whilst Ct2 represents the



−VOC+

Rs

Rt1 Rt2

Ct1

Vt1+ −

Ct2

Vt2+ −+

−

V (t)

I(t)

Figure 5.2: Dual polarisation (DP) Thévenin model

concentration polarisation, which is used to characterise the transient response of the bat-tery. According to [14], the DP Thévenin model presents the best dynamic performanceand offers a more accurate SOC estimation compared to other ECMs. In [44], a similarderivation was performed for the DP model, by transforming the state space model ofthe battery to the frequency domain. The derivation presented here is however muchsimpler, since all the equations are derived within the time domain, as shown below:

V (t) = VOC −RsI(t)− Vt1(t)− Vt2(t) (5.2.4)dV (t)

dt= −Rs

dI(t)

dt− dVt1(t)

dt− dVt2(t)

dt(5.2.5)

d2V (t)

dt2= −Rs

d2I(t)

dt2− d2Vt1(t)

dt2− d2Vt2(t)

dt2(5.2.6)

I(t) =Vt1(t)

Rt1

+ Ct1dVt1(t)

dt(5.2.7)

dI(t)

dt=

1

Rt1

dVt1(t)

dt+ Ct1

d2Vt1(t)

dt2(5.2.8)

I(t) =Vt2(t)

Rt2

+ Ct2dVt2(t)

dt(5.2.9)

dI(t)

dt=

1

Rt2

dVt2(t)

dt+ Ct2

d2Vt2(t)

dt2(5.2.10)

Again the assumption is made that the open circuit voltage, VOC , will remain constantover one sampling period. Equations (5.2.4) through (5.2.10) can be solved simultaneouslyusing a MATLAB solver function, as shown in Appendix B.2, to find V (t) in terms ofVOC and the other ECM parameters such that

V (t) = VOC − (Rs +Rt1 +Rt2)I(t)

− (Rs(Ct1Rt1 + Ct2Rt2) +Rt1Rt2(Ct1 + Ct2))dI(t)

dt

− Ct1Ct2Rt1Rt2RsdI2(t)

dt2− (Ct1Rt1 + Ct2Rt2)

dV (t)

dt

− Ct1Ct2Rt1Rt2dV 2(t)

dt2. (5.2.11)



In order to further simplify the above equation let Ct1Rt1=τt1 and Ct2Rt2=τt2 where τt1and τt2 are the time constants of the two RC pairs of the model, resulting in the simplifiedequation

V (t) = VOC − (Rs +Rt1 +Rt2)I(t)

− (Rs(τt1 + τt2) +Rt2τt1 +Rt1τt2)dI(t)

dt

−Rsτt1τt2dI2(t)

dt2− (τt1 + τt2)

dV (t)

dt− τt1τt2

dV 2(t)

dt2. (5.2.12)

5.3 Recursive Least Squares Algorithm

5.3.1 Introduction

The following section discusses the rewriting of the derived ECM mathematics into theform required by the RLS algorithm. The RLS algorithm is divided into 8 steps tosimplify the implementation thereof.

5.3.2 Recursive structure

The DP Thévenin ECM mathematics need to be rewritten into the required format forthe RLS algorithm in order to simplify the calculations. This is achieved by setting

V (t) = φT (t)θ, (5.3.1)

where the input vector is

φT (t) =[1 I(t) dI(t)

dtd2I(t)dt2

dV (t)dt

d2V (t)dt2

], (5.3.2)

and the parameters that needs to be estimated using the RLS algorithm is

θ =[θ1 θ2 θ3 θ4 θ5 θ6

]T

=

VOC

−(Rs +Rt1 +Rt2)−(Rs(τt1 + τt2) +Rt2τt1 +Rt1τt2)

−Rsτt1τt2−(τt1 + τt2)−τt1τt2

. (5.3.3)

In the case of an on-line application, the output V (t) and the input I(t) are discretisedat a constant sampling period T . Therefore, (5.3.1) requires to be converted into thediscrete form such that

V [k] = φT [k]θ. (5.3.4)

Within (5.3.2), I(t) and V (t) are discretised as I[k] and V [k] and represents thecurrent and voltage measured at a constant sample period T . The other variables can be



approximated by:

dI(t)

dt≈ I[k]− I[k − 1]

T, (5.3.5)

d2I(t)

dt2≈ I[k]− 2I[k − 1] + I[k − 2]

T 2, (5.3.6)

dV (t)

dt≈ V [k]− V [k − 1]

T, (5.3.7)

d2V (t)

dt2≈ V [k]− 2V [k − 1] + V [k − 2]

T 2. (5.3.8)

Once the vector θ is identified by the RLS algorithm, the parameters of the batterycan then be derived from (5.3.3) as follows:

VOC = θ1, (5.3.9)

Rs =θ4θ6, (5.3.10)

τt1 = −θ5 −√

(θ25 + 4θ6)

2, (5.3.11)

τt2 = −θ5 +√

(θ25 + 4θ6)

2, (5.3.12)

Rt1 =−1

θ25θ6 + 4θ26[2θ4θ6 + 2θ2θ

26 + θ3θ5θ6 + θ2θ

25θ6

+ τt2(2θ3θ6 − θ4θ5 + θ2θ5θ6)],

(5.3.13)

Rt2 =−1

θ25θ6 + 4θ26[2θ4θ6 + 2θ2θ

26 − θ3θ5θ6 + θ4θ

25

− τt2(2θ3θ6 − θ4θ5 + θ2θ5θ6)].

(5.3.14)

5.3.3 Recursive identification algorithm

As stated the RLS algorithm can be divided into 8 steps. By repeating these steps eachtime a new set of input and output data is measured, the RLS algorithm minimizes theestimated error. The algorithm should be initialised with the initial estimate of θ0 = 0and P0 = (106)I, where θ0 is the initial estimation, P0 is the initial covariance matrixand I is an identity matrix. This will place the emphasis on the incoming data insteadof on the input vector θ0.

In the 8 steps described below, K[k] is the recursive gain and P[k] is the covariancematrix, while λ is the forgetting factor (FF) of the RLS algorithm. The steps are asfollow:

1. Initialise parameters θ0 and P0.

2. Collect the input data I[k], I[k − 1]), I[k − 2] and the output data V [k], V [k − 1],V [k − 2].

3. Calculate φT [k].



4. Calculate the recursive gain matrix

K[k] =P[k − 1]φ[k]

λ+ φT [k]P[k − 1]φ[k]. (5.3.15)

5. Calculate the system output prediction

V [k] = φT [k]θ[k − 1]. (5.3.16)

6. Calculate the error between the measured output and the predicted output withthe previous estimated parameter vector values

E[k] = V [k]− V [k]. (5.3.17)

7. Calculate a new updated estimate of the system parameter vector

θ[k] = θ[k − 1] + E[k]K[k]. (5.3.18)

8. Calculate covariance matrix

P[k] =P[k − 1]−K[k]φ[k]P[k − 1]

λ. (5.3.19)

The battery parameters tend to change over time. Therefore, the dependence of theparameters on temperature, SOC and battery degradation is managed via the forgettingfactor as in (5.3.19). A small FF places more emphasis on new incoming data, whilea large FF places greater emphasis on older data. Typically the FF is chosen between0.95 and 1. If the value chosen for the FF is too small, the system will become unstable.Similarly, if λ = 1, the FF will not work at all, since older measurements are weighed thesame as new measurements.

5.4 Simulation and results

5.4.1 Introduction

In this section the suggested DP Thévenin ECM mathematics are proven using a simu-lation. The simulation can be split in two parts:

1. The ECM is simulated in Simulink to provide input (current) and output (voltage)data.

2. This data is then used, in an on-line manner by the RLS algorithm in order toestimate the parameters of the battery.

By comparing the parameters used inside the Simulink simulation with the estimatedparameters a conclusion can be drawn on whether the mathematical analysis is correct.



5.4.2 Simulation

The Simulink simulation is discussed within this section. A block diagram of the Simulinksimulation is shown in Figure 5.3. The current is integrated to monitor the SOC of thebattery during discharge. A gain factor is used to scale this value to a percentage of thetotal battery capacity. This value is subtracted from the initial SOC in order to calculatethe remaining SOC of the battery. This SOC value is then fed into a polynomial function,which describes the relationship between the SOC and OC voltage, in order to supply areference OC voltage output to the voltage source. This results in a voltage, VOC(SOC),which is a voltage source controlled by a polynomial that describes the relationship be-tween the SOC and the OC voltage. The polynomial of the Shell Eco-marathon (SEM)battery pack is used in this simulation. The relationship was determined by [16] and willbe discussed in more detail in Chapter 6. The sampling period T is chosen as 1 s.

InitialSOC

+

− SOCpolynomial −

+VOC(SOC)

Gain

Integrator

currentRs Rt1 Rt2

Ct1

Vt1+ −

Ct2

Vt2+ −+

Rload

SidealPWM

In

-Ibat

Vbat Noise

Figure 5.3: Simulink simulation

The battery model parameters that were used is shown in Table 5.1.

Table 5.1: Simulink model parameters

Parameter ValueRs 1 ΩRt1 0.5 ΩRt2 0.5 ΩCt1 100 FCt2 20 F

The current profile at which the simulated battery model is discharged, is shown inFigure 5.4. The simulated battery is discharged from 95% to 15%. This ensures that thenon-linear relationship between the OC voltage and the SOC would be present in theoutput of the simulation. The current profile comprises of the sum of two components:



0 100 200 300 400 500 600 700

0

2

4

Time [s]

Current[A

]

Total battery I

Resistor I

Figure 5.4: Battery discharge profile

1. A discharge pulse through a resistor. The pulse has a period of 720 seconds and aduty cycle of 0.45.

2. A varying current to excite the dynamic behaviour of the battery. This varyingcurrent is added to the simulation by using a current source that is driven by noise.

In Figure 5.4 the resistor current is shown in red and the total battery current isshown in blue. The varying current is added to provide the system with some excitationduring periods when almost no dynamic behaviour is detected such as during constantcurrent periods. According to [45], poor excitation can lead to the exponential growth ofthe covariance matrix (or covariance wind-up). This results in the estimator becomingextremely sensitive and susceptible to numerical and computational errors.

5.4.3 Simulation results

The simulated measured output voltage (blue) and the estimated voltage (red) is presentin Figure 5.5. The error between the output voltage and estimated voltage, as a percent-age, can be seen in Figure 5.6. It is clear that the RLS algorithm effectively minimises theerror between output voltage and estimated voltage. The maximum error between thevoltage and estimated voltage is smaller than 1%. This proves that the RLS algorithmwas implemented successfully.

The estimated ECM parameters for the battery is shown in Figure 5.7. In each subfigure, the RLS algorithm’s estimation is indicated in red, while the Simulink simulationparameter value is indicated in blue. The estimated parameters compare closely withthe actual simulated parameters when the OC voltage is a constant value as shown inFigure 5.7a through 5.7f.

The parameters are unstable when the OC voltage of the battery changes too quickly.This is due to the RLS algorithm presented here inherently assume a model with a con-stant OC voltage. According to [17] the estimation response of the OC voltage is slow



0 100 200 300 400 500 600 700

45

50

55

Time [s]

Voltage[V

]

V

RLS estimated V

Figure 5.5: Estimated Voltage

0 1,000 2,000 3,000 4,000 5,000

−1

0

1

Time [s]

Estim

ationerror[%

]

Figure 5.6: Voltage estimation error

compared to that of the typical drive-cycle excitation dynamics. Typically, the RLS algo-rithm is combined with a current integration method since the two methods complementeach other. The purpose of the simulation was firstly to prove the mathematical modelwas correctly derived and secondly to test the implementation of the RLS algorithm.

The sampling period was also shown to have a significant impact on the accuracyof the estimation. There is a trade-off between the sampling time and the maximumvalue of the time constants of the ECM that can be estimated. The larger the samplingtime, the larger the time constants that can be estimated. This is due to the fact thatthe estimation rests on only three voltage and current measurements at a time. Forinstance, when the time constants of the model are very long and the sampling time isshort, the differences in the output are too small to measure. Under these circumstancesthe RLS algorithm cannot perform the estimation accurately. Increasing the sampling



0 1,000 2,000 3,000 4,000 5,00049

51

53

55

Time [s]

VOC

[V]

VOC

RLS estimated VOC

(a) VOC estimation

0 1,000 2,000 3,000 4,000 5,0000.6

0.7

0.8

0.9

1

1.1

1.2

Time [s]

Rs

[Ω]

RLS estimated Rs

Rs

(b) Rs estimation

0 1,000 2,000 3,000 4,000 5,0000

20

40

60

80

Time [s]

τ t1[s]

RLS estimated τt1τt1

(c) τt1 estimation

0 1,000 2,000 3,000 4,000 5,0000

0.25

0.5

0.75

1

Time [s]

Rt1

[Ω]

RLS estimated Rt1

Rt1

(d) Rt1 estimation

0 1,000 2,000 3,000 4,000 5,0000

2

4

6

8

10

Time [s]

τ t2[s]

RLS estimated τt2τt2

(e) τt2 estimation

0 1,000 2,000 3,000 4,000 5,0000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Time [s]

Rt2

[Ω]

RLS estimated Rt2

Rt2

(f) Rt2 estimation

Figure 5.7: Estimates of the RLS algorithm compared to the actual parameters



time too much on the other hand, impacts the assumption that the open circuit voltage,VOC , remains constant over one sampling period. The RLS method’s accuracy is alsoreduced by selecting a large sampling time. In practice, it is therefore important thatthe optimum sampling time is used. The optimal sampling time varies from one cellchemistry to another, but typically a sampling time of 1 - 2 seconds are used.

It is clear that the characteristics of the battery influence the accuracy that the practi-cal system requires in order to successfully implement the RLS algorithm. The simulationwas adjusted to investigate the accuracy required by a practical system, to accuratelyestimate the battery parameters using the RLS algorithm. This was achieved by addinga quantisation error to the input (current) and output (voltage) data. A quantisationerror typically occurs when the measurements are measured with an ADC. The simulateddata is rounded off to the third decimal in order to achieve a quantization error largeenough to affect the output of the RLS algorithm. This effect can be seen in Figure 5.8as an example of the RLS algorithm’s behaviour during such an input. It is clear thatthe RLS algorithm is extremely sensitive to low resolution data, since a 0.1mV accuracyis required on a voltage signal with an amplitude of up to 50V. This sensitivity is furthersupported by the fact that the covariance matrix of the current system used in the RLSalgorithm is ill-conditioned. Thus, a small change in input data has a great effect on theoutput data of the algorithm.

0 1,000 2,000 3,000 4,000 5,0000

2

4

6

8

10

Time [s]

τ t2[s]

T2 estimation

RLS est. τt2τt2RLS est. τt2 with quantization

Figure 5.8: τt2 quantization error result

5.5 ConclusionThis chapter discussed the mathematical derivation of an ECM to the RLS algorithm’sformat. The battery model was simulated in Simulink. The generated data was used incombination with the RLS algorithm in order to estimate the parameters of the simulatedbattery parameters in an on-line manner. The derivation was proven to be accurate andthe RLS algorithm was implemented successfully.



The designed BMS’s accuracy unfortunately does not comply with the required spec-ifications for the RLS algorithm. This resulted in the on-line estimation being inaccurateand can thus not be implemented in the BMS. Further work will need to be conducted inorder to make the algorithm function properly with less accurate data. Other work thatneed to be investigated includes the minimisation of the noise on the measured data. Itmight be necessary to extend the current RLS algorithm into the recursive extended leastsquares (RELS) algorithm and or the Kalman filter in order to limit the impact of noise.


Chapter 6

Results

6.1 IntroductionThis section discusses the results of all the different components designed within thisthesis. This includes the SSC as well as the different components for the BMS.

6.2 Battery Management SystemThis section investigates the performance of all the different components within the BMS.This includes the voltage and current measurements and how these measurements areaffected.

6.2.1 BQ76940 Analogue to Digital Converter Accuracy

The voltage measurements obtained using the BQ76940 battery monitoring chip arediscussed within this section. After initial testing, the discovery was made that theBQ76940’s ADC measurement output at certain voltage references remains constant eventhough the voltage changes. An example of this behaviour is shown in Figure 6.1. Themeasurements were obtained after a discharge pulse during which the cells were giventime to reach equilibrium. All the cell voltages rise as can be expected. The measuredcell voltages also rise but at certain reference voltages are clamped to a constant valueas is shown in Figure 6.1. The reference value varies from cell to cell. This behaviour atone cell also does not influence the measurement of the other cell voltages. The sourceof this behaviour was investigated but was not found.

It is important to note that this non-linear behaviour’s impact on the accuracy of themeasurements are within the accuracy of the BQ76940 chip. A curve was fitted to themeasured data in order to calculate the voltage measurement error more accurately. Themaximum error was found to be 4mV, which is within the accuracy specification of theBQ76940 battery monitoring chip.

66


CHAPTER 6. RESULTS 67

50800 51000 51200 51400 51600 51800 52000Time [s]

3.185

3.190

3.195

3.200

3.205

3.210

3.215

3.220

Volta

ge[V

]

cell 2cell 3cell 4cell 5

Figure 6.1: ADC error

6.2.2 Measurement Noise

This section investigates the measurement noise of the proof of concept BMS and thefull scale BMS. A lot of effort was put into minimising both the voltage and currentmeasurement noise on the full scale BMS. This included upgrading to the TPS54060buck regulator. The buck regulator has a small voltage ripple which in turn reduces thenoise induced on to the cell voltage and battery current measurements. The full scaleBMS design also makes use of a 4 layer PCB to minimise noise, where one layer was usedfor grounding and another layer was used for supply power to the PCB.

6.2.2.1 Voltage Measurements

The cell voltage measurement noise of the proof of concept BMS is compared to thatof the full scale BMS in this subsection. Unfortunately, the BQ76940 cannot measure avoltage of 0V. Therefore, the voltage measurement noise are isolated off-line by means ofa filter. The Python code is shown in Appendix B.3. Both signals are filtered with exactlythe same filter: A Butterworth filter with a sample rate of 1 Hz and a cut-off frequencyof 0.01 Hz. The filtered data is subtracted from to the measured data to estimate thenoise. The estimated noise of both systems are shown in Figure 6.2. It is clear that thefull scale BMS’s measurement noise is lower than that of the prototype BMS.



0 200 400 600 800 1000Time [s]

−0.005

−0.004

−0.003

−0.002

−0.001

0.000

0.001

0.002

0.003

0.004Vo

ltage

[V]

Proof of concept BMS noiseFull scale BMS noise

Figure 6.2: Voltage measurement noise comparison

The power within the signals are calculated using

P =1

N

N−1∑n=0

|x[n]|2. (6.2.1)

The above equation quantifies the difference in power of the two noise signals. Theprototype BMS was calculated to have a noise power level of 21.9 µW/Ω compared to10.7 µW/Ω of the full scale BMS which confirms that the noise is attenuated in the fullscale design.

6.2.2.2 Current Measurements

One of the main motivations for minimising the voltage ripple of the supply voltage is tominimise the current measurement noise. The sensor used converts the analogue currentmeasurement into a reference voltage that is then measured using the MCU’s ADC. Acurrent value of up to ±150A is scaled down to a corresponding voltage of between zeroand the supply voltage of 3.3V. This makes the sensor extremely sensitive to noise. Thesensor output at a current of 0A is shown in Figure 6.3. The magnitude of the noise is1.6A. This results in a 0.53% error which is well within the desired error range of 1%.

In order to further decrease the noise, an infinite impulse response (IIR) filter isapplied in software. A simple low pass filter is used with the difference equation given inthe time domain by

y(k) = y(k − 1) + a[x(k)− y(k − 1)].

The variables y(k) and x(k) refer to the output and input data, respectively, while a =1− e−ωcTs . The cut-off frequency is selected as 4Hz and the corresponding filter outputis shown in Figure 6.4. The cut-off frequency is chosen according to the cell voltage



0 100 200 300 400 500Samples

−1.0

−0.5

0.0

0.5

1.0

1.5

Cur

rent

[A]

Noise

Figure 6.3: Current sensor noise

sampling frequency. The measurement noise is reduced to 150mA, which is less than10% of the original magnitude.

100 200 300 400 500Samples

−0.05

0.00

0.05

0.10

Cur

rent

[A]

Noise

Figure 6.4: Filtered current sensor noise



6.2.3 Current Sensor Thermal Test Result

A thermal test was conducted at a constant current of 116A, at room temperature,for a time of 5 minutes. A Fluke thermal camera was used to measure the maximumtemperature of the board and the result is shown in Figure 6.5. A temperature riseof 21C was measured. The temperature is seen to rise higher than anticipated due tothe power dissipated within the internal resistance of the current sensor, which was notincluded into the temperature calculation. The temperature rise is however still withinthe safe range of operation.

Figure 6.5: Current sensor thermal performance

6.2.4 Terminal Connections

The high current rating of the battery proposes various problems. One such problem isthe amount of contact required between the battery terminal and connector. A test wasundertaken to investigate the influence of a weak terminal connection. The battery packunderwent a 66A pulse discharge test. The cell measurements of the surrounding cellsare shown in Figure 6.6.

Cell 1 was purposefully connected with a weak terminal connection while cell 2 andcell 3 where connected normally. From Figure 6.6 it is clear that the cell voltage ofcell 1 is greatly affected compared to cell 2 and cell 3. The weak terminal connectionintroduces a high contact resistance. When large amount of current flows into or fromthe battery terminal, the high contact resistance dissipates a large amount of power. Thisgenerates heat at the terminal of the battery, which in turn reduces the available capacityof the cell. The voltage across the contact resistance also forces the two parallel cells todischarge unevenly. As soon as the connection is tightened, the two cells are shown toreach equilibrium again. This test also shows that basic algorithms can be used to ensurethat all the terminal connections are connected properly, by measuring the difference incell voltage.



13000 14000 15000 16000 17000 18000 19000Time [s]

2.6

2.7

2.8

2.9

3.0

3.1

3.2

3.3

Volta

ge[V

]

cell 1cell 2cell 3

Figure 6.6: Weak terminal connections

6.2.5 Balancing

This section discusses the performance of the balancing circuits and the balancing al-gorithm. Cell balancing is implemented to maximise the usable capacity of the batterypack. The balancing was implemented according to the balancing algorithm discussed inChapter 4. The balancing reference voltage was chosen as 3.45V. This resulted in thecells being balanced until their voltage falls below the chosen reference voltage, as shownin Figure 6.7. Additional cell measurements can be seen in Appendix B.4.

The cell voltage reference value can be further reduced if the cells require more bal-ancing. Unfortunately, this will also result in a longer balancing time. The cell balancing,for this test, was achieved within 36 minutes. Further field testing is however required inorder to find the optimal balancing current and time, since these parameters are typicallydependant upon the application that the battery is used for.

During balancing cell 6 and cell 11’s voltage measurement are influenced as shown inFigure 6.8. This can be attributed to the operation of the BQ79640 battery monitoringchip. It is internally divided into three sections, each working independently. Each ofthese sections monitors five cells and are stacked on top of each other to achieve an overallcell count of 15. While cell balancing is active, these sections typically turn off balancingfor a short duration of time to measure the cell voltages. Unfortunately, in some instancesthese dead-times are not synchronised between the different sections. It is thus possiblefor one cell to be measured while another cell is being balanced. Take for instance cell10 and cell 11 in Figure 6.8, where a clear influence is notable during balancing.

All the terminal connections connected to the main control PCB are fused except theground connection. Cell voltages are measured from the one positive terminal connectionto the positive connection of the next cell’s positive terminal to reduce the amount ofconductors connected to the main control PCB.



0 500 1000 1500 2000

Time [s]

3.35

3.40

3.45

3.50

3.55

3.60V

olta

ge[V

]

cell 3cell 4cell 5

Figure 6.7: Cell balancing

500 1000 1500 2000

Time [s]

3.40

3.45

3.50

3.55

3.60

3.65

3.70

Vol

tage

[V]

cell 10cell 11

Figure 6.8: Impact of protection fuse during cell balancing



In this instance the balancing current of cell 10 flows through a 2A fuse, which affectsthe voltage measurement of cell 11. The fuse has an internal resistance of 50mΩ. Abalancing current of 1A thus results in a 50mV voltage offset, as seen in Figure 6.8. For-tunately, this behaviour can be ignored by implementing software changes. The analogueovervoltage threshold of the BQ76940 chip is also adjusted to account for this possiblevoltage offset. The fuse can be moved within the circuitry or can be replaced by a fusewith a smaller resistance, in future iterations of the BMS, to solve this problem.

6.3 Battery Off-line Parameter Estimation

6.3.1 Introduction

In this section the off-line parameter estimation of the DP Thévenin ECM is estimated forthe LFP battery pack. Firstly, the total battery pack capacity was validated accordingto the standard discharge test of the battery manufacturer. The capacity was determinedas only 150Ah, which is 75% of the rated 200Ah capacity specified. A later discussionwith the Chinese manufacturer revealed that they sent the wrong capacity cells. The85Ah version of the cells where shipped, not the 100Ah cells. This is very peculiarsince the manufacturer claimed that both versions have the same physical size and thatcurrently the 100Ah cells are out of stock. Unfortunately, this discussion only occurredafter the off-line tests were already conducted and the battery was returned to the microelectric vehicle manufacturer. For these reasons all the tests were performed at the 150Ahcapacity.

The DP Thévenin ECM is again chosen for off-line parameter estimation due to itshigh accuracy. For convenience the model is repeated here and shown in Figure 6.9. Themodel has six different parameters that require to be estimated. All these parameters areestimated according to [16]. The test consists of pulse discharging the battery through aconstant load. Unfortunately, no HPPC tests where concluded since during the testingphase, there where no high power chargers available. The hysteresis effect of the LFPcells were also ignored, as in [16].

The voltage of the battery during the pulse discharge test is shown in Figure 6.10. Theduration for the battery to reach equilibrium was determined by discharging the batteryat a constant rate before giving it time to settle at a constant voltage. The duration forthe dynamic behaviour of the battery to reach 98% of it’s final value was determined as

VOC(SOC)

Rs

Rt1 Rt2

Ct1

Vt1+ −

Ct2

Vt2+ −+

−

V(t)

I(t)

Figure 6.9: Dual polarization Thévenin equivalent circuit model



0 10000 20000 30000 40000 50000 60000Time [s]

42

44

46

48

50

52Vo

ltage

[V]

Voltage

Figure 6.10: Pulse discharge test

90 minutes. The datasheet’s standard discharge current (66A) was chosen as the averagedischarge rate of the test. The battery was discharged 10% at a time. The coulombcounter of the BMS was used during discharge to monitor the SOC of the battery.

6.3.2 Parameter Estimation

The estimation of the model parameters are discussed in the following section. The SOCrelationship with the OC voltage is shown in Figure 6.11. A polynomial curve fit wasimplemented in order to estimate a continuous relationship between the two variables.

Secondly, resistor Rs was calculated by using the immediate voltage rise during onesample period after the contactor opened, while the current was measured just before thecontactor opened. The assumption was made that for a step input, the the dynamic effectsover one sampling period is negligibly small compared to that of the static componentsin the model. The resistance Rs is shown in Figure 6.12 with respect to SOC. FromFigure 6.12 it can be seen that the battery’s ohmic resistance increases at low- and highSOC. The magnitude of the resistor Rs rises 13% from its minimum to maximum value.

Lastly, the remaining parameters were estimated using Matlab’s curve fit toolbox.The dynamic behaviour of the battery is approximated by

Vdynamic(t) = Ibat[Rt1e

−tRt1Ct1 +Rt2e

−tRt2Ct2 ]. (6.3.1)

A curve fit of this form is applied onto the battery data. As an example, the curvefit of the dynamic behaviour at 90% SOC is shown in Figure 6.13. It is clear that thecurve fit approximates the battery’s behaviour accurately. This confirms the model’sability to accurately model the battery dynamics. The required parameters were then



0 20 40 60 80 100State of charge [%]

44

46

48

50

52O

pen

circ

uitv

olta

ge[V

]

Open circuit voltage datasetPolynomial curve fit

Figure 6.11: SOC vs OC voltage

extracted from the curve fit result. The approximated resistance Rt1 and Rt2 is shown inFigure 6.14.

The total battery resistance, Rtotal = Rs + Rt1 + Rt2, is shown in Figure 6.15. Thebattery’s maximum internal resistance, as expected, is found at a low SOC. The inter-nal resistance decreases as the SOC increases up to 60%, where the minimum internal

10 20 30 40 50 60 70 80 90State of charge [%]

0.0100

0.0105

0.0110

0.0115

0.0120

0.0125

Res

ista

nce

[Ω]

Ohmic battery resistance Rs

Figure 6.12: Ohmic resistance characteristic curve of the battery



0 1000 2000 3000 4000 5000 6000State of charge [%]

0.0

0.2

0.4

0.6

0.8

Tim

eco

nsta

nt[s

]

Battery dynamic behaviourCurve fit

Figure 6.13: Curve fit of dynamic behaviour

10 20 30 40 50 60 70 80 90State of charge [%]

0.004

0.006

0.008

0.010

0.012

Res

ista

nce

[Ω]

Concentration polarization resistance Rt2

Electrochemical polarization resistance Rt1

Figure 6.14: Dynamic resistance characteristic curve of the battery



resistance is found. This is typically where Li-ion batteries are most stable. The internalresistance increases gradually as the SOC increases to 100%. The magnitude of the totalinternal resistance rises 37.5% from its minimum to maximum value.

10 20 30 40 50 60 70 80 90State of charge [%]

0.018

0.020

0.022

0.024

0.026

0.028

Res

ista

nce

[Ω]

Total battery resistance

Figure 6.15: Total battery resistance characteristic curve

The two estimated time constants, τt1 and τt2, is shown in Figure 6.16 and 6.17respectively. τt1 and τt2 represents the time constants RT1CT1 and RT2CT2. Time constantτt1 stays relatively constant with a rise of 9% from its minimum to maximum value. Thetime constant τt2 varies significantly over the discharge cycle, up to 41% from its minimumto maximum value. It is not uncommon for the concentration polarisation to vary to thismagnitude as can also be seen in [16].



10 20 30 40 50 60 70 80 90State of charge [%]

1250

1300

1350

1400

1450

1500Ti

me

cons

tant

[s]

Time constant t1

Figure 6.16: Time constant τt1 characteristic curve

10 20 30 40 50 60 70 80 90State of charge [%]

50

55

60

65

70

75

80

85

90

Tim

eco

nsta

nt[s

]

Time constant t2

Figure 6.17: Time constant τt2 characteristic curve



6.4 Solid State ContactorIn this section the performance of the SSC is discussed. This includes the normal opera-tion, fault conditions and thermal analysis of the SSC.

6.4.1 Test Setup

The performance of the designed SSC is investigated within the following subsection. Thetest set-up is shown in Figure 6.18. A 48V lead-acid battery is used to deliver the requiredcurrent through the SSC. Fuse F1 is used to limit the battery current in case of a faultcondition. Switch S1 is an emergency switch that can be used to disconnect the batteryfrom the load in the case of an emergency. The current through the SSC is measuredwith a current probe. The SSC is powered by a small 12V supply. A signal generatoris used to generate an input to the SSC. Diode D1 is used as a freewheeling diode whenthe SSC is opened in order to provide a path for the current to flow. This decreases theamount of power that is to be dissipated by the TVS within the SSC. Resistor R1 is avariable resistor that is used to control the magnitude of the current flowing through theSSC. The high current carrying components are indicated with thicker lines.

48 V

400 A

F1

S1

Current probe

SSC

D1 R1

Supply

Input

Figure 6.18: SSC test set-up

6.4.2 Normal Operation

The SSC’s normal operation is tested first. The SSC is required to operate at a steadystate current of up to 120A without the overcurrent protection tripping. The resistiveload is setup to allow a maximum continuous current of 120A. The SSC is closed by thesignal generator. The SSC current and the input voltage to the MOSFET gate driver areshown in Figure 6.19. It is clear that the overcurrent was not activated.

The current sense pin voltage and the current reference pin voltage of the MOSFETdriver are shown in Figure 6.20. The trip condition is activated when the current sensevalue exceeds the reference value minus 25mV as stated by the datasheet of the UCD7100MOSFET driver. The current sense value remains below this reference value as shown



−0.0001 0.0000 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006Time [s]

0

20

40

60

80

100

120C

urre

nt[A

]

SSC currentSSC gate driver input

0

2

4

6

8

10

12

Volta

ge[V

]

Figure 6.19: SSC current at maximum load

−0.00005 0.00000 0.00005 0.00010 0.00015 0.00020Time [s]

−0.1

0.0

0.1

0.2

0.3

0.4

0.5

Volta

ge[V

]

Current referenceCurrent sense

Figure 6.20: Current sense and reference pin at maximum load



in Figure 6.20. It can be seen that the reference value is affected by the large change incurrent of the SSC. As soon as the SSC current settles at a constant value, the referencevalue also settles. The change in current induces a voltage on the reference pin. Thiscoupling effect will have to be eliminated in the next iteration of the SSC.

The thermal test of the SSC is performed at a constant current of 120A. The testis conducted at room temperature. The temperature of the the SSC is measured witha Fluke thermal camera. The SSC is closed for a period of five minutes before thetemperature is measured. The test results of the thermal camera is shown in Figure 6.21.The SSC reaches a maximum temperature of up to 51C around the MOSFETs. Thisresults in a temperature rise of about 26C which confirms the thermal design. Thethermal design was implemented for a maximum temperature rise of 30C. The openPCB areas were not included in the thermal design, even though it does contain thermalconduction. This explains why the temperature rise is lower than anticipated.

Figure 6.21: SSC temperature at maximum load

6.4.3 Overcurrent Trip

This section investigates the SSC’s ability to open in the case of an overcurrent condition.This feature is of great importance in order to make the SSC more robust. A mechanicalcontactor is very robust in the sense that its maximum failing current is much higherthan its rated current. For example, the contactor used for this project has a maximumbreaking capacity of eight times its rated capacity. The BMS thus has a relatively longtime period to react in the case of a fault condition.

Unfortunately, the SSC is much more sensitive. When the current that is required tobe disconnected is too large, the risk of the MOSFETs not closing increases. This thusmakes the SSC less robust. The SSC has over an overcurrent trip with a fast reactiontime for this reason. This feature protects the SSC from a short circuit fault as well asfrom thermal runaway.

The resistive load, for this test, is decreased until the SSC’s overcurrent trip is acti-vated. Decreasing the resistive load increases the SSC current. An example of the SSCovercurrent trip is shown in Figure 6.22. It opens at a current of 133A even though the



−0.00005 0.00000 0.00005 0.00010 0.00015 0.00020Time [s]

0

20

40

60

80

100

120C

urre

nt[A

]

SSC currentSSC gate driver input

0

5

10

15

Volta

ge[V

]

Figure 6.22: SSC trip

input to the system is still active. The input is required to be reset in order to resetthe SSC. This proves that the resistance of the MOSFETs can be used, to relative accu-racy, to measure the current flowing through them. The feedback loop also performed asdesigned. The SSC’s overcurrent trip protects it from a fault condition.

The current sense pin voltage and the current reference pin voltage of the MOSFETdriver is shown in Figure 6.23. The trip condition is shown to be activated when thecurrent sense value is larger than the reference value minus 25mV. This leads to theMOSFET driver opening the MOSFETs. The change in current that induces a voltageon the reference pin can once again be seen. Fortunately, it is not large enough to impactthe operation of the SSC.

Both the reference and sense pins of the MOSFET driver, experience a transientresponse during the initial start of switching, as shown in Figure 6.24. The source ofthese oscillations were not found. It is present at no load conditions as well as at a lowerMOSFET switching time. This will need to be investigated further in future work.

The dead-time of the current sense pin voltage is also shown in Figure 6.24. The deadtime is 800 ns, which confirms the design choice of a dead-time below 1µs.

One of the concerns with using MOSFETs to break a current, is how to protect theMOSFET’s terminal voltage. The MOSFET typically switch very fast, which could leadto a voltage spike on the terminal of the MOSFET (VDS) because of the inductance withinthe conductors. Transient voltage suppressors (TVSs) are therefore used to ensure theMOSFETs are protected against overvoltage conditions. The current and voltage of theSSC are shown in Figure 6.25, during turn-off. There is a voltage spike at the beginning assoon as the MOSFETs close. The voltage spike activates the TVSs. The TVSs conducts



−0.00005 0.00000 0.00005 0.00010 0.00015 0.00020Time [s]

−0.1

0.0

0.1

0.2

0.3

0.4

0.5Vo

ltage

[V]

Current referenceCurrent sense

Figure 6.23: Current sense and reference pin

0.0000005 0.0000010 0.0000015 0.0000020 0.0000025Time [s]

−0.2

0.0

0.2

0.4

0.6

Volta

ge[V

]

Current senseCurrent reference

Figure 6.24: Dead time



−0.00001 0.00000 0.00001 0.00002 0.00003 0.00004 0.00005Time [s]

0

20

40

60

80

100

120C

urre

nt[A

]SSC currentSSC terminal voltage

0

20

40

60

80

100

120

Volta

ge[V

]

Figure 6.25: SSC during turn-off

the current until the current reaches zero, thus effectively clamping the voltage below themaximum voltage of 100V. When the current reaches zero, the TVSs deactivate until thenext turn-off cycle.

6.5 ConclusionThis section discussed the various results of the BMS. The BMS’s voltage and currentmeasurement noise were investigated, both of which were reduced due to design changesimplemented from the proof of concept design BMS. The balancing algorithm was imple-mented within the BMS and the BMS was successfully used to balance the LFP batterypack. The BMS was also successfully used to estimate the DP Thévenin ECM parametersof the LFP battery pack in an off-line manner.

This section also discussed the various results of the SSC. The SSC’s overcurrenttrip function was implemented and it successfully protected the SSC from an overcurrentcondition. The thermal design of the SSC was verified. The overvoltage protection whilebreaking the rated current of the SSC was also validated.


Chapter 7

Conclusion

7.1 IntroductionThe conclusion to the different components and algorithms investigated within this thesisare discussed in the following section. Possible future work is also discussed.

7.2 ConclusionAn overview is presented to show how the thesis addressed the objectives as set out inChapter 1. These were:

• Development of a proof of concept Li-ion BMS to ensure the basic working principleof the BMS.

• Development of a full scale Li-ion BMS for the purpose of a micro EV.

• The successful implementation of a balancing algorithm within the BMS.

• Off-line parameter estimation using the BMS.

• The development of an on-line parameter estimation algorithm using an appropriatebattery model.

• Development of a proof of concept SSC.

7.2.1 Development of a proof of concept Li-ion BMS to ensurethe basic working of the BMS

The proof of concept BMS was designed, manufactured and tested. The system func-tioned with sufficient performance and proved the operation of the various subsystems.The system was shown to be ideal to monitor small battery packs. It also provideda good basic understanding of BMSs and good insight into what was required for thedevelopment of the full scale BMS.

85


CHAPTER 7. CONCLUSION 86

7.2.2 Development of a full scale Li-ion BMS for the purpose ofa micro EV

The full scale BMS was also designed, manufactured and tested. The system deliveredimprovements in performance compared to that of the proof of concept version. Thiswas mainly due to the full scale BMS’s ability to minimise the supply voltage ripple.All of the different subsystems performed according to the design specifications. Thesesubsystems include the current sensor circuit, balancing circuit and main control circuitof the full scale BMS.

7.2.3 The successful implementation of a balancing algorithmwithin the BMS

Passive battery cell balancing was successfully implemented within the BMS. Balancingwas achieved by the integration of the hardware design as well as the software design.

7.2.4 Off-line parameter estimation using the BMS

The BMS was used to monitor the states of the battery during a pulse discharge cycle.This data was used off-line to estimate the parameters of a DP Thévenin ECM. Theseparameters can be used in future work to monitor for example how the battery ages overtime.

7.2.5 The development of an on-line parameter estimationalgorithm using an appropriate battery model

A derivation of the DP Thévenin ECM mathematics to the RLS algorithm format waspresented in Chapter 5. A Simulink simulation was used to simulate a DP Thévenin ECMthrough a discharge cycle. The derivation was confirmed with the RLS algorithm in anon-line manner. The RLS algorithm was found to be accurate, but extremely sensitiveto measurement accuracy and noise.

7.2.6 Development of proof of concept SSC

A proof of concept SSC was designed, manufactured and tested. The performance of theSSC, in terms of efficiency, was shown to be competitive compared to that of the me-chanical contactor. The SSC was shown to be more energy efficient within the operatingconditions that the SSC was designed for. Unfortunately, it is very expensive comparedto the mechanical contactor.

7.3 Future work

7.3.1 Battery management system

The addition of an optional real time calibration system can be added to increase themeasurement accuracy of the BQ76940. This typically entails externally calibrating the


CHAPTER 7. CONCLUSION 87

measurements using the host MCU. Further software development can also decrease thepower consumption of the BMS for example using the MCU’s sleep mode.

The current sensor’s noise sensitivity can also be reduced by powering it with 5V andnot 3.3V. At 3.3V it typically produces a measurement offset. This will unfortunatelyresult in having an extra supply rail.

7.3.1.1 Battery cell balancing

The optimal magnitude of the balancing current will need to be investigated for thisspecific BMS. This will possibly lead to increasing the battery balancing current to shortenthe battery balancing time. Other balancing algorithms can also be investigated to findan algorithm that can balance the cells while the battery is actively being used.

The 2A protection fuse used in the balancing circuit’s design will have to be recon-sidered. This could possibly result in moving the fuse or to chose a fuse with a smallerinternal resistance.

7.3.2 On-line parameter estimation

Methodologies for making the RLS algorithm less susceptible to measurement accuracywill have to be investigated in future work. The impact of noise will also have to beinvestigated. This could include upgrading the RLS algorithm to a Kalman filter.

7.3.3 Solid state contactor

The cost of the SSC will need to be reduced in order to make it a financially viableoption. This will have to be investigated in future work. It could typically be achievedby decreasing the size of the PCB and reducing the amount of MOSFETs used. Thedesign needs to be optimised in terms of cost and efficiency with the specific load inmind. The transient response of the current feedback also requires to be investigated andminimised. This can lead to the SSC being more robust.


Bibliography

[1] M. Taylor, K. Daniel, A. Ilas, and E. Young, “Renewable Power Generation Costsin 2014,” International Renewable Energy Agency (IRENA), Tech. Rep., 2015.[Online]. Available: www.irena.org/publications

[2] R. Xiong, F. Sun, X. Gong, and C. Gao, “A data-driven based adaptive state ofcharge estimator of lithium-ion polymer battery used in electric vehicles,” AppliedEnergy, vol. 113, pp. 1421–1433, 2014.

[3] Energy Storage Association, “Redox Flow Batteries.” [Online]. Available:http://energystorage.org/energy-storage/technologies/redox-flow-batteries

[4] Cornell University, “Battery Anodes.” [Online]. Available: http://www.emc2.cornell.edu/content/view/battery-anodes.html

[5] F. Badin, Hybrid Vehicles From Components to System. Editions Tech-nip, 2013. [Online]. Available: http://gen.lib.rus.ec/book/index.php?md5=AAD1D2CA3838E8CAD1D1416B8EB23F7A

[6] J. Molenda and M. Mole, “Composite Cathode Material for Li-Ion Batteries Based onLiFePO4 System.” in Metal, Ceramic and Polymeric Composites for Various Uses.InTech, jul 2011, ch. 30.

[7] B. Scrosati, J. Garche, and W. Tillmetz, Advances in Battery Technologies for Elec-tric Vehicles, 1st ed. Woodhead Publishing, 2015.

[8] M. Bingeman and B. Jeppesen, “Improving Battery Management System Perfor-mance and Cost with Altera FPGAs,” Altera, Tech. Rep. January, 2015.

[9] A. Hausmann and C. Depcik, “Expanding the Peukert equation for battery capacitymodeling through inclusion of a temperature dependency,” Journal of Power Sources,vol. 235, pp. 148–158, 2013.

[10] L. Qian, Y. Si, and L. Qiu, “SOC estimation of LiFePO4 Li-ion battery using BPNeural Network,” in 28th International Electric Vehicle Symposium and Exhibition,2015, pp. 1–7. [Online]. Available: http://www.a3ps.at/site/sites/default/files/downloads/evs28/papers/A4-03.pdf

[11] D. Jiani, L. Zhitao, W. Youyi, and W. Changyun, “A fuzzy logic-basedmodel for Li-ion battery with SOC and temperature effect,” in 11th IEEEInternational Conference on Control & Automation (ICCA). IEEE, jun 2014, pp.

88


www.irena.org/publications

http://energystorage.org/energy-storage/technologies/redox-flow-batteries

http://www.emc2.cornell.edu/content/view/battery-anodes.html

http://www.emc2.cornell.edu/content/view/battery-anodes.html

http://gen.lib.rus.ec/book/index.php?md5=AAD1D2CA3838E8CAD1D1416B8EB23F7A

http://gen.lib.rus.ec/book/index.php?md5=AAD1D2CA3838E8CAD1D1416B8EB23F7A

http://www.a3ps.at/site/sites/default/files/downloads/evs28/papers/A4-03.pdf

http://www.a3ps.at/site/sites/default/files/downloads/evs28/papers/A4-03.pdf

BIBLIOGRAPHY 89

1333–1338. [Online]. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6871117

[12] J. Jiuchun and Z. Caiping, Fundamentals and Application of Lithium-ion Batteriesin Electric Drive Vehicles, 1st ed. Wiley, 2015. [Online]. Available: http://gen.lib.rus.ec/book/index.php?md5=11AA3536910E17170FF614920CB688D4

[13] “FreedomCAR Battery Test Manual For Power-Assist Hybrid Electric Vehicles,”Idaho National Engineering and Environmental Laboratory, Tech. Rep., 2003.

[14] H. He, R. Xiong, and J. Fan, “Evaluation of Lithium-Ion Battery EquivalentCircuit Models for State of Charge Estimation by an Experimental Approach,”Energies, vol. 4, no. 12, pp. 582–598, mar 2011. [Online]. Available:http://www.mdpi.com/1996-1073/4/4/582/

[15] H. Hongwen He, R. Rui Xiong, X. Xiaowei Zhang, F. Fengchun Sun, andJ. JinXin Fan, “State-of-Charge Estimation of the Lithium-Ion Battery Usingan Adaptive Extended Kalman Filter Based on an Improved Thevenin Model,”IEEE Transactions on Vehicular Technology, vol. 60, no. 4, pp. 1461–1469, may2011. [Online]. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5739545

[16] D. Gandolfo, A. Brandão, D. Patiño, and M. Molina, “Dynamic model of lithiumpolymer battery: Load resistor method for electric parameters identification,” Jour-nal of the Energy Institute, vol. 88, no. 4, pp. 470–479, 2015.

[17] A. Emadi, Advanced Electric Drive Vehicles. CRC Press, 2014.

[18] “EIS Measurement of a Very Low Impedance LithiumIon Battery,” Gamry instruments, Tech. Rep., 2011.[Online]. Available: http://www.gamry.com/application-notes/EIS/eis-measurement-of-a-very-low-impedance-lithium-ion-battery/

[19] L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A review on the key issues forlithium-ion battery management in electric vehicles,” Journal of Power Sources, vol.226, pp. 272–288, 2013.

[20] D. Andre, C. Appel, T. Soczka-Guth, and D. U. Sauer, “Advanced mathematicalmethods of SOC and SOH estimation for lithium-ion batteries,” Journal of PowerSources, vol. 224, pp. 20–27, 2013.

[21] M. Corno, N. Bhatt, S. M. Savaresi, and M. Verhaegen, “Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells,” IEEE Transactions on ControlSystems Technology, vol. 23, no. 1, pp. 117–127, jan 2015. [Online]. Available:http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6786991

[22] J. Xu, B. Cao, J. Cao, Z. Zou, C. C. Mi, and Z. Chen, “A Comparison Studyof the Model Based SOC Estimation Methods for Lithium-Ion Batteries,” in 2013IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, oct 2013, pp.1–5. [Online]. Available: http://ieeexplore.ieee.org/document/6671653/


http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6871117


http://gen.lib.rus.ec/book/index.php?md5=11AA3536910E17170FF614920CB688D4

http://gen.lib.rus.ec/book/index.php?md5=11AA3536910E17170FF614920CB688D4

http://www.mdpi.com/1996-1073/4/4/582/



http://www.gamry.com/application-notes/EIS/eis-measurement-of-a-very-low-impedance-lithium-ion-battery/

http://www.gamry.com/application-notes/EIS/eis-measurement-of-a-very-low-impedance-lithium-ion-battery/


http://ieeexplore.ieee.org/document/6671653/

BIBLIOGRAPHY 90

[23] L. W. Juang, P. J. Kollmeyer, T. M. Jahns, and R. D. Lorenz, “Implementationof online battery state-of-power and state-of-function estimation in electric vehicleapplications,” in 2012 IEEE Energy Conversion Congress and Exposition (ECCE).IEEE, sep 2012, pp. 1819–1826. [Online]. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6342591

[24] Analog Devices, “Lithium Ion Battery Monitoring System AD7280A,” 2011.[Online]. Available: http://www.analog.com/media/en/technical-documentation/data-sheets/AD7280A.pdf

[25] Linear Technology, “Multicell Battery Stack Monitor LTC6802-1.” [Online].Available: http://cds.linear.com/docs/en/datasheet/68021fa.pdf

[26] Texas Instruments, “bq769x0 3-Series to 15-Series Cell Battery MonitorFamily for Li-Ion and Phosphate Applications,” 2013. [Online]. Available:http://www.ti.com/lit/ds/slusbk2g/slusbk2g.pdf

[27] I. Aizpuru, U. Iraola, J. M. Canales, M. Echeverria, and I. Gil, “Passivebalancing design for Li-ion battery packs based on single cell experimentaltests for a CCCV charging mode,” in 2013 International Conference on CleanElectrical Power (ICCEP). IEEE, jun 2013, pp. 93–98. [Online]. Available:http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6586973

[28] Texas Instruments, “TMS320F2803x Piccolo Microcontrollers,” 2009. [Online].Available: http://www.ti.com/lit/ds/symlink/tms320f28035.pdf

[29] ——, “Constant On-Time Synchronous Buck Regulator LM5017,” 2012. [Online].Available: http://www.ti.com/lit/ds/symlink/lm5017.pdf

[30] ——, “LMX9838 Bluetooth Serial Port Module,” 2007. [Online]. Available:http://www.ti.com/lit/ds/snosaz9e/snosaz9e.pdf

[31] ——, “SN65HVD23x 3.3-V CAN Bus Transceivers,” 2002. [Online]. Available:http://www.ti.com/lit/ds/symlink/sn65hvd235.pdf

[32] Future Technology Devices International Ltd, “UMFT201XB, UMFT220XB andUMFT230XB Datasheet.” [Online]. Available: http://www.ftdichip.com/Support/Documents/DataSheets/Modules/DS_UMFT201_220_230XB.pdf

[33] Texas Instruments, “TPS54060 Step Down DC-DC Converter,” 2009. [Online].Available: http://www.ti.com/lit/ds/symlink/tps54060.pdf

[34] D. G. Brooks and J. Adam, “Trace Currents and Temperatures Revisited,” Tech.Rep., 2015.

[35] Allegro MicroSystems, “ACS758 Hall Effect-Based Linear Current Sensor.” [On-line]. Available: http://www.allegromicro.com/en/Products/Current-Sensor-ICs/Fifty-To-Two-Hundred-Amp-Integrated-Conductor-Sensor-ICs/ACS758.aspx

[36] Albright International, “SW80 D.C. contactor.” [Online]. Available: http://www.albrightinternational.com/products/sw80/




http://www.analog.com/media/en/technical-documentation/data-sheets/AD7280A.pdf

http://www.analog.com/media/en/technical-documentation/data-sheets/AD7280A.pdf

http://cds.linear.com/docs/en/datasheet/68021fa.pdf

http://www.ti.com/lit/ds/slusbk2g/slusbk2g.pdf


http://www.ti.com/lit/ds/symlink/tms320f28035.pdf

http://www.ti.com/lit/ds/symlink/lm5017.pdf

http://www.ti.com/lit/ds/snosaz9e/snosaz9e.pdf

http://www.ti.com/lit/ds/symlink/sn65hvd235.pdf

http://www.ftdichip.com/Support/Documents/DataSheets/Modules/DS_UMFT201_220_230XB.pdf

http://www.ftdichip.com/Support/Documents/DataSheets/Modules/DS_UMFT201_220_230XB.pdf

http://www.ti.com/lit/ds/symlink/tps54060.pdf

http://www.allegromicro.com/en/Products/Current-Sensor-ICs/Fifty-To-Two-Hundred-Amp-Integrated-Conductor-Sensor-ICs/ACS758.aspx

http://www.allegromicro.com/en/Products/Current-Sensor-ICs/Fifty-To-Two-Hundred-Amp-Integrated-Conductor-Sensor-ICs/ACS758.aspx

http://www.albrightinternational.com/products/sw80/

http://www.albrightinternational.com/products/sw80/

BIBLIOGRAPHY 91

[37] Murata Power Solutions, “Isolated 1W Single Output DC/DC ConvertersMEU1 Series.” [Online]. Available: http://power.murata.com/data/power/ncl/kdc_meu1.pdf

[38] Fairchild, “General purpose phototransistor optocouplers 4N35.” [Online]. Available:https://www.fairchildsemi.com/datasheets/4N/4N35M.pdf

[39] Micro Commercial Components, “Transient Voltage SuppressionDiodes.” [Online]. Available: http://pdf.datasheet.company/datasheets-1/micro_commercial_components/5KP54CA-AP.pdf

[40] Texas Instruments, “Digital Power Control Driver UCD7100.” [Online]. Available:http://www.ti.com/product/UCD7100/technicaldocuments

[41] H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo, “Online model-basedestimation of state-of-charge and open-circuit voltage of lithium-ion batteries inelectric vehicles,” Energy, vol. 39, no. 1, pp. 310–318, 2012. [Online]. Available:http://dx.doi.org/10.1016/j.energy.2012.01.009

[42] Y.-H. Chiang, W.-Y. Sean, and J.-C. Ke, “Online estimation of internal resistanceand open-circuit voltage of lithium-ion batteries in electric vehicles,” Journal ofPower Sources, vol. 196, no. 8, pp. 3921–3932, 2011.

[43] T. Feng, L. Yang, X. Zhao, H. Zhang, and J. Qiang, “Online identificationof lithium-ion battery parameters based on an improved equivalent-circuitmodel and its implementation on battery state-of-power prediction,” Journalof Power Sources, vol. 281, pp. 192–203, 2015. [Online]. Available: http://dx.doi.org/10.1016/j.jpowsour.2015.01.154

[44] Xidong Tang, Xiaofeng Mao, Jian Lin, and B. Koch, “Li-ion batteryparameter estimation for state of charge,” in Proceedings of the 2011American Control Conference. IEEE, jun 2011, pp. 941–946. [Online]. Available:http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5990963

[45] A. Vahidi, A. Stefanopoulou, and H. Peng, “Recursive least squares with forgettingfor online estimation of vehicle mass and road grade: theory and experiments,”Vehicle System Dynamics, vol. 43, no. 1, pp. 31–55, jan 2005. [Online]. Available:http://www.tandfonline.com/doi/abs/10.1080/00423110412331290446


http://power.murata.com/data/power/ncl/kdc_meu1.pdf

http://power.murata.com/data/power/ncl/kdc_meu1.pdf

https://www.fairchildsemi.com/datasheets/4N/4N35M.pdf

http://pdf.datasheet.company/datasheets-1/micro_commercial_components/5KP54CA-AP.pdf

http://pdf.datasheet.company/datasheets-1/micro_commercial_components/5KP54CA-AP.pdf

http://www.ti.com/product/UCD7100/technicaldocuments

http://dx.doi.org/10.1016/j.energy.2012.01.009

http://dx.doi.org/10.1016/j.jpowsour.2015.01.154

http://dx.doi.org/10.1016/j.jpowsour.2015.01.154


http://www.tandfonline.com/doi/abs/10.1080/00423110412331290446

Appendices

92


Appendix A

Calculations

A.1 LM5017 regulator designDesign specifications: Maximum input voltage of 54.6 V with a minimum input voltageof 41.6 V. The regulated output voltage is 5 V. Design is done for a maximum outputcurrent of 500 mA. The design can be seen below:

A.1.1 Feedback resistor selection

VOUT = VFB x (RFB2

RFB1

+ 1)

where VFB = 1.225 V. Standard values of 3 kΩ and 1 kΩ are chosen for RFB2 and RFB1

to result in an output voltage of 4.9 V.

A.1.2 Frequency selection

fSW (MAX) =1−DMAX

TOFF (MIN)

=1− 5/41.6

200 ns= 4.4 MHz

fSW (MAX) =DMIN

TON(MIN)

=5/54.6

100 ns= 0.916 MHz

A conservative switching frequency of 200 kHz is selected.

fSW =VOUTK.RON

= 200 kHz

RON =VOUTK.fSW

=5

1x10−10.200x103= 250 kΩ

RON is chosen smaller at 220 KΩ to ensure the switching frequency is always largerthan the reference 200 KHz.

93


APPENDIX A. CALCULATIONS 94

A.1.3 Inductor selection

The minimum inductance is selected to limit the output ripple to 15 % of the maximumload current.

∆IL =VIN − VOUTL1.fSW

xVOUTVIN

The maximum ripple is observed at maximum input voltage. Substituting VIN = 54.6V and ∆IL = 15 % x IOUT (MAX) results in L1 = 3.028 µH. The next higher standardvalue of 330 µH is chosen.

A.1.4 Output capacitor selection

The output capacitor is selected to minimise the output voltage ripple. The ripple canbe calculated by:

COUT =∆IL

8.fSW .∆Vripple

A 22 µF ceramic capacitor is chosen. This results into a voltage ripple of 2.13 mV.

A.1.5 Input Capacitor selection

The input capacitor is selected to minimise the input voltage ripple. The ripple can becalculated by:

CIN ≥IOUT (MAX)

8.fSW .∆VIN

Two 2.2 µF ceramic capacitors are placed in parallel to result in a total of 4.4 µF.This results into a voltage ripple of 82 mV.

A.1.6 Undervoltage lockout resistors selection

These resistors set the undervoltage lockout (UVLO) and hysteresis according to:

VIN(HY S) = IHY S x RUV 2

andVIN(UV LO, rising) = 1.225 x (

RUV 2

RUV 1+ 1)

where IHY S = 20 µA. RUV 1 is chosen as 6.8 kΩ and RUV 2 as 120 kΩ. This result in aUVLO threshold and hysteresis of 22.8 V and 2.4 V respectively.

A.1.7 Ripple configuration selection

The minimum ripple configuration is chosen to minimise the output voltage ripple. Thevalues are chosen as follows:

Cr = 3.3 nF


APPENDIX A. CALCULATIONS 95

Cac = 100 nF

Rr are chosen according to

RcCr ≤(VIN(MIN) − VOUT ) x TON

25 mV

which result in 100 kΩ.


Appendix B

Code

B.1 Python USB listener

"""Created on Wed May 04 16:38:09 2016@author: B. Horn"""import serialimport structimport csv

try:ser = serial.Serial(port=’COM8’,baudrate=9600,parity=serial.PARITY_NONE,stopbits=serial.STOPBITS_ONE,bytesize=serial.EIGHTBITS,xonxoff=False,timeout=5# set input buffer size = read_size # doesn’t seem necessary in pyserial

)except serial.SerialException:

print ’Could not open port’, ’port’ + ’.’print ’Use \’dmesg -T | grep -i usb\’ to find appropriate ports.’

myfile = open(’document.csv’, ’ab’)

try:while 1:

outputList = []while 1:

data_in = bytearray()while not data_in:

data_in = ser.read(size=4)

96


APPENDIX B. CODE 97

if len(data_in) == 4:(float_out,) = struct.unpack(’>f’, data_in) #>foutputList.append(float_out);data_in = 0#print ’Output received and stored:’#print float_out

if len(outputList) == 18:#append write to csvwr = csv.writer(myfile, dialect=’excel’)wr.writerow(outputList)myFormattedList = [ ’%.3f’ % elem for elem in outputList ]#print ’Output received and stored:’print myFormattedListoutputList[:] = []break

except KeyboardInterrupt:myfile.close()ser.close()

B.2 MATLAB Symbolic solver

syms Voc Rin Rp Rq Cp Cq Vt dVt dV2t I1 dI d2I Vp Vq dVp dVq dV2p dV2q

sol = solve(’-Vt + Voc - I1*(Rin) - Vp - Vq’,’dVt +dI*Rin + dVp + dVq’,’dV2t +d2I*Rin + dV2p + dV2q’,’Vp/Rp + dVp*Cp - I1’,’ dVp/Rp + dV2p*Cp -dI’,’-I1 +Vq/Rq + dVq*Cq’,’-dI + dVq/Rq + dV2q*Cq’,’Vp’ ,’Vq’ , ’dVp’ , ’dVq’,’dV2p’, ’dV2q’,’Vt’);

sol.Vt

%Voc - I1*Rin - I1*Rp - Cp*Rp*dVt - Cp*Rin*Rp*dI%Voc%- I1*Rin - I1*Rp - I1*Rq%- Cp*Rin*Rp*dI - Cq*Rin*Rq*dI - Cp*Rp*Rq*dI - Cq*Rp*Rq*dI%- Cp*Cq*Rin*Rp*Rq*d2I%- Cp*Rp*dVt - Cq*Rq*dVt%- Cp*Cq*Rp*Rq*dV2t

syms Theta2 Theta3 Theta4 Theta5 Theta6 Tp Tq

sol2 = solve(’Theta2+Rin+Rp+Rq’,’Theta3+Rin*(Tp+Tq)+Rq*Tp+Rp*Tq’,’Theta4 +Tp*Tq*Rin’,’Theta5 + Tp+Tq’,’Theta6 +Tp*Tq’, ’Rin’ , ’Rq’, ’Rp’, ’Tp’,’Tq’);


APPENDIX B. CODE 98

% sol2 = solve(’Theta2+Rin+Rp+Rq’,’Theta3 + Cp*Rin*Rp + Cq*Rin*Rq + Cp*Rp*Rq +Cq*Rp*Rq’,’Theta4 + Cp*Cq*Rin*Rp*Rq’,’Theta5 + Cp*Rp + Cq*Rq’,’Theta6+Cp*Cq*Rp*Rq’, ’Rin’ , ’Rq’, ’Rp’, ’Cp’,’Cq’);

sol2.Rinsol2.Tpsol2.Tqsol2.Rpsol2.Rq

B.3 Noise filter

B.3.1 Noise filter Python code

# -*- coding: utf-8 -*-"""Created on Fri May 27 13:31:24 2016

@author: B. Horn"""import numpy as npfrom scipy.signal import butter, lfilter, freqz, filtfiltimport matplotlib.pyplot as plt

def butter_lowpass(cutoff, fs, order=5):nyq = 0.5 * fsnormal_cutoff = cutoff / nyqb, a = butter(order, normal_cutoff, btype=’low’, analog=False)return b, a

def butter_lowpass_filter(data, cutoff, fs, order=5):b, a = butter_lowpass(cutoff, fs, order=order)z = filtfilt(b, a, data)return z

Voltage,current =loadtxt(’Data_lipo.CSV’,delimiter=’,’,skiprows=1,usecols=(0,1), unpack = 1)

current2, Voltage2 = loadtxt(’test6_long.CSV’,delimiter=’,’,skiprows=0,usecols=(15,17), unpack = 1)

order = 1fs = 1 # sample rate, Hzcutoff = 0.01 # desired cutoff frequency of the filter, Hz

y = butter_lowpass_filter(Voltage, cutoff, fs, order)


APPENDIX B. CODE 99

x = butter_lowpass_filter(Voltage2, cutoff, fs, order)

Lipo = Voltage[11800:12800]Lipo_filt = y[11800:12800]

LFP = Voltage2[28000:29000]LFP_filt = x[28000:29000]

noise1 = Lipo_filt-Liponoise2 = LFP_filt-LFP

noise11 = (sum((abs(noise1))**2))/sys.getsizeof(noise1)noise22 = (sum((abs(noise2))**2))/sys.getsizeof(noise2)

B.3.2 Noise filter result

0 20 40 60 80 100Time [s]

−0.005

−0.004

−0.003

−0.002

−0.001

0.000

0.001

0.002

0.003

0.004

Volta

ge[V

]

Prototype BMS noiseUp-scaled BMS noise

Figure B.1: Noise of prototype BMS compared to that of the up-scaled BMS


APPENDIX B. CODE 100

B.4 Battery balancing

500 1000 1500 2000Time [s]

3.35

3.40

3.45

3.50

3.55

3.60

Volta

ge[V

]


Figure B.2: Battery balancing

500 1000 1500 2000Time [s]

3.35

3.40

3.45

3.50

3.55

3.60

3.65

3.70

Volta

ge[V

]




APPENDIX B. CODE 101

500 1000 1500 2000Time [s]

3.35

3.40

3.45

3.50

3.55

3.60

Volta

ge[V

]cell 12cell 13cell 14cell 15



Appendix C

PCB Schematics

C.1 Full Scale BMS Schematic

C.1.1 BMS Control Schematic

11

22

33

44

55

66

DD

CC

BB

AA

3

Mel

lowC

abs

Fran

shoe

kSo

uth

Afric

a

7

BMS

31.

120

16/1

1/22

09:3

9:54

PM

C:\U

sers

\166

8781

7\D

ropb

ox\M

eest

ers\

PCB

des

igns

\PC

B 1

.1\P

WR

524A

_AFE

1.Sc

hDoc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:Ta

bloi

dB

arth

o H

orn

Engi

neer

:

DSG

1C

HG

2

VSS

3

SDA

4SC

L5

TS1

6C

AP1

7R

EGO

UT

8R

EGSR

C9

VC

5X10

NC

11

NC

12

TS2

13C

AP2

14

VC

10X

15

NC

16

NC

17

TS3

18C

AP3

19B

AT

20

NC

21N

C22

NC

23

VC

1524

VC

1425

VC

1326

VC

1227

VC

1128

VC

10B

29

VC

1030

VC

931

VC

832

VC

733

VC

634

VC

5B35

VC

536

VC

437

VC

338

VC

239

VC

140

VC

041

SRP

42

SRN

43

ALE

RT

44

U1

PartNum

ber

GN

D

GN

D

GN

D

GN

D

C5

C4

C3

C1

C0

C2

C6

C7

C8

C9

GN

D

C10

C8

C7

C6

C5

C4

C3

C2

C1

C0

C0

ALE

RT

SRN

SRP

VC

10B

VC

11V

C12

VC

13V

C14

VC

15

BA

T

REG

SRC

CH

GD

SG

VC

10X

CA

P1

REG

OU

T

SCL

SDA

C1

C2

C3

C4

C5

C7

C6

C9

VC

5X

VC5X

VC

5X

REG

SRC

VC

5X

GN

D

GN

D

BA

TT+

C9

C8

123

P3 3 w

ay 2

.54

mol

ex C5F

C10

FC

15F(

48V

)C

15F(

48V

)

C5F

C10

F

BQ

_Ena

ble

2.2u

FC

19

1KR2

4.7K

R3

Z2 Zene

r

1 2

T3

1 2

T2

1 2

T1

3.3n

FC

16

3.3n

FC

17

3.3n

FC

18

1µF

C13

1µF

C14

1µF

C15

1KR1

C5F

SM4T

6V7A

YD

1

4.7u

FC

20

SM4T

6V7A

YD

2

SM4T

6V7A

YD

3

SM4T

6V7A

YD

4

SM4T

6V7A

YD

5

SM4T

6V7A

YD

6

SM4T

6V7A

YD

7

SM4T

6V7A

YD

8

1234

POW

ER

4 W

ay 2

.54m

m m

olex

hea

der

123

D12

BA

V99

SM4T

6V7A

Y

D11

SM4T

6V7A

Y

D9

SM4T

6V7A

Y

D10

123

P2 3 w

ay 2

.54

mol

ex

123

P1 3 w

ay 2

.54

mol

ex

C10

Z1 Zene

r

470nF

C2

470nF

C12

470nF

C11

470nF

C10

470nF

C9

470nF

C8

470nF

C1

470nF

C7

470nF

C6

470nF

C5

470nF

C4

470nF

C3

G1

S2

D3

N ty

pe

U2B

PIC101 PIC102COC

1

PIC201 PIC202COC

2

PIC301 PIC302COC

3

PIC401 PIC402COC

4

PIC501 PIC502COC

5

PIC601 PIC602COC

6

PIC701 PIC702COC

7

PIC801 PIC802CO

C8

PIC901 PIC902COC

9

PIC1001 PIC1002CO

C10

PIC1101 PIC1102CO

C11

PIC1201 PIC1202CO

C12

PIC1301 PIC1302COC13

PIC1401 PIC1402CO

C14

PIC1501 PIC1502CO

C15


PIC1701 PIC1702COC17 PIC1801 PIC1802

COC18


PIC2001PIC2002COC20

PID101PID102COD

1

PID201PID202CO

D2

PID301PID302COD

3

PID401PID402COD

4

PID501PID502CO

D5

PID601PID602COD

6

PID701PID702CO

D7

PID801PID802COD

8

PID901

PID902

COD9

PID1001

PID1

002

COD10

PID1101

PID1

102

COD11

PID1

201

PID1

202

PID1

203CO

D12

PIP101

PIP102

PIP103

COP1

PIP201

PIP202

PIP203

COP2

PIP301

PIP302

PIP303

COP3

PIPO

WER0

1

PIPOWER02

PIPO

WER0

3

PIPO

WER0

4

COPOWER

PIR101

PIR1

02COR1

PIR2

01PIR202COR2

PIR301PIR302 COR3

PIT101

PIT102 COT

1

PIT201

PIT202 COT

2

PIT301

PIT302 COT

3

PIU101

PIU102

PIU103

PIU104

PIU105

PIU106

PIU107

PIU108

PIU109

PIU1

010

PIU1

011

PIU1

012

PIU1

013

PIU1

014

PIU1

015

PIU1

016

PIU1

017

PIU1

018

PIU1

019

PIU1020

PIU1

021

PIU1

022

PIU1

023

PIU1024

PIU1

025

PIU1

026

PIU1

027

PIU1

028

PIU1

029

PIU1

030

PIU1

031

PIU1

032

PIU1

033

PIU1034

PIU1

035

PIU1

036

PIU1037

PIU1

038

PIU1

039

PIU1

040

PIU1041

PIU1042

PIU1

043

PIU1

044CO

U1

PIU201

PIU202

PIU203

COU2

B

PIZ101PIZ103COZ

1

PIZ201PIZ203COZ

2PI

U104

4

PID1

102

PIU1020

PIU203

PID1

201

PID801PI

POWE

R01

PIR1

02NLC

0

PIC1102 PIC1201

PID701 PID802

PIP301

PIU1

040

NLC1

PIC1002 PIC1101

PID601 PID702PIP302

PIU1

039

NLC2

PIC902 PIC1001

PID501 PID602

PIP303

PIU1

038

NLC3

PIC802 PIC901

PID502

PIP201

PIU1037

NLC4

PIC801

PID901

PIP202

PIU1

036

NLC5

PID401

PIPOWER02

PIR202

NLC5F

PIC602 PIC701

PID301 PID402

PIP203

PIU1034

NLC6

PIC502 PIC601

PID201 PID302

PIP101

PIU1

033

NLC7

PIC402 PIC501

PID101 PID202

PIP102

PIU1

032

NLC8

PIC302 PIC401PID102

PIP103

PIU1

031

NLC9

PIC301

PID1001

PIU1

030

NLC10

PIPO

WER0

3NL

C10F

PIPO

WER0

4NL

C15F

(48V

)

PIC1301

PIU107

PIU102

PIU101

PIC202

PIC1202

PIC1302

PIC1802

PIC1902

PIC2001PIT102

PIU103

PIZ201

PIC101PI

R201

PIU1

035

PIZ103

PIC201PIR101

PIU1041

PIC1401

PIU1

016

PIU1

017

PIU1

019

PIC1501

PIU1

011

PIU1

012

PIU1

014

PIC1601PIT301

PIU1

018

PIC1701PIT201

PIU1

013

PIC1801

PIR302

PIT101

PIU106

PIZ203

PID1

202

PIR301PI

D120

3

PIU1

021

PIU1

022

PIU1

023

PIC2002

PIU108

PIC1901

PIU109

PIU202

PIU105

PIU104

PIU1

043

PIU1042

PIC102

PIC702PIC1502

PIC1702

PID902

PIT202

PIU1

010

PIU201

PIZ101NLVC5X

PIU1

029

PIC1402PIC1602

PID1

002

PIT302

PIU1

015

PIU1

028

PIU1

027

PIU1

026

PIU1

025

PID1101

PIU1024

102


APPENDIX C. PCB SCHEMATICS 103

11

22

33

44

55

66

DD

CC

BB

AA

4

Mel

lowc

abs

Fran

shoe

kSo

uth

Afric

a

7

BMS

41.

120

16/1

1/22

09:5

0:18

PM

C:\U

sers

\166

8781

7\D

ropb

ox\M

eest

ers\

PCB

des

igns

\PC

B 1

.1\P

WR

524A

_AFE

2.Sc

hDoc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:Ta

bloi

dB

arth

o H

orn

Engi

neer

:

10µF

C29

C12

C13

C14

C15

GN

D

C10

F

BA

TT+

C5F

VC

15V

C14

VC

13V

C12

VC

11V

C10

B

VC

10X

VC

5X

BA

T

C15

F(48

V)

1KR4

1KR5

1KR6

1KR7

10µF

C27 10

µFC28

470nF

C22

C4

C9

SM4T

6V7A

YD

15

SM4T

6V7A

YD

16

SM4T

6V7A

YD

17

SM4T

6V7A

YD

18

SM4T

6V7A

YD

19

SM4T

6V7A

YD

13

SM4T

6V7A

YD

14

123

P4 3 w

ay 2

.54

mol

ex

C10

C10

C11

123

P5 3 w

ay 2

.54

mol

ex

Z3 Zene

r

470nF

C23

470nF

C24

470nF

C25

470nF

C26

470nF

C21

PIC2101 PIC2102CO

C21

PIC2201 PIC2202CO

C22

PIC2301 PIC2302CO

C23

PIC2401 PIC2402CO

C24

PIC2501 PIC2502CO

C25

PIC2601 PIC2602CO

C26

PIC2701 PIC2702CO

C27

PIC2801 PIC2802CO

C28

PIC2901 PIC2902CO

C29

PID1301PID1302CO

D13

PID1401PID1402CO

D14

PID1501PID1502CO

D15

PID1601PID1602CO

D16

PID1701PID1702CO

D17

PID1801PID1802CO

D18

PID1901PID1902CO

D19

PIP401

PIP402

PIP403

COP4

PIP501

PIP502

PIP503

COP5

PIR4

01PI

R402COR

4

PIR5

01PI

R502COR

5

PIR601

PIR602COR

6

PIR7

01PI

R702COR

7

PIZ301PIZ303COZ

3

PIC2901

PIR7

01PI

R702

PID1401PID1402PI

R402

PID1301

PIP501

NLC1

0

PID1302

PID1901

PIR5

02

PIR602

PIC2502 PIC2601

PID1801 PID1902

PIP502

NLC1

1

PIC2402 PIC2501

PID1701 PID1802

PIP503

NLC1

2

PIC2302 PIC2401

PID1601 PID1702

PIP401

NLC1

3

PIC2202 PIC2301

PID1501 PID1602

PIP402

NLC1

4

PIC2201PID1502

PIP403

NLC1

5

PIC2802

PIC2702

PIC2801PI

R401

PIC2101PIR601

PIZ303PIC2102

PIC2602

PIC2701

PIC2902

PIR5

01

PIZ301



11

22

33

44

55

66

DD

CC

BB

AA

5

Mel

lowC

abs

Fran

shoe

kSo

uth

Afric

a

7

BMS

51.

120

16/1

1/22

09:5

2:38

PM

C:\U

sers

\166

8781

7\D

ropb

ox\M

eest

ers\

PCB

des

igns

\PC

B 1

.1\P

WR

524A

_BQ

7835

0DB

T.Sc

hDoc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:Ta

bloi

dB

arth

o H

orn

Engi

neer

:

SCL

SDA

ALE

RT

12K

R16

300K

R11

30.9

K

R22

3.3V

22uFC

42

10nF

C35

BA

TT+

GPI

O22

1

GPI

O32

2

GPI

O33

3

GPI

O23

4

GPI

O42

5

GPI

O43

6

VD

D7

VSS

8

nXR

S9

nTR

ST10

AD

CIN

A7

11

AD

CIN

A6

12

AD

CIN

A5

13

AD

CIN

A4

14

AD

CIN

A3

15

AD

CIN

A2

16

AD

CIN

A1

17

AD

CIN

A0

18

Vre

fhi

19

VD

DA

20

VSS

A21

Vre

flo22

AD

CIN

B0

23

AD

CIN

B1

24

AD

CIN

B2

25

AD

CIN

B3

26

AD

CIN

B4

27

AD

CIN

B5

28

AD

CIN

B6

29

AD

CIN

B7

30

GPI

O27

31

CA

NTX

32

CA

NR

X33

SCL

34

VSS

35

VD

DIO

36

GPI

O26

37

Test

238

GPI

O9

39

SDA

40G

PIO

1841

GPI

O17

42G

PIO

843

GPI

O25

44G

PIO

4445

GPI

O16

46G

PIO

1247

GPI

O41

48G

PIO

749

GPI

O6

50X

251

X1

52V

SS53

VD

D54

GPI

O19

55G

PIO

3956

GPI

O38

57G

PIO

3758

GPI

O35

59G

PIO

3660

GPI

O11

61G

PIO

562

GPI

O4

63G

PIO

4064

GPI

O10

65G

PIO

366

GPI

O2

67G

PIO

168

GPI

O0

69V

DD

IO70

VSS

71V

DD

72V

rege

nz73

GPI

O34

74G

PIO

1575

GPI

O13

76G

PIO

1477

GPI

O20

78G

PIO

2179

GPI

O24

80U

3

F280

35

GN

D

GN

D

GN

D

GN

D

3.3V

GN

D

GN

D

3.3V

12

34

56

78

910

1112

1314

P6 Hea

der 7

X2

3.3V

TCK

TMS

TDI

TDO

3.3V

TMS

TDI

TDO

TCK

2.2K

R13

GN

D GN

D

GN

D

3.3n

F

C37

TRST

TRST

3.3V

GN

D2.2u

FC

31

3.3V

3.3V

3.3V

GN

DG

ND

Driv

e_se

nse

Key

GN

D

LED

1LE

D2

LED

3

LED

1LE

D2

LED

3

SCI-

rece

ive

SCI-

trans

mit

BT1

Can

TxC

anR

x

D1

GN

D2

Vcc

3

R4

EN5

CL

6C

H7

Rs

8U

6

CA

N-S

N65

HV

D23

4

GN

D3.

3V

CA

N_E

N

CA

N_E

N

CA

NTx

CA

NR

xG

ND

GN

D 10K

R43

GN

D

62R46

BQ

_Ena

ble

GPI

O_0

1

GPI

O_1

2

GPI

O_2

3

GPI

O_3

4

RES

ET_L

5

SER

IAL_

46

SER

IAL_

37

SER

IAL_

28

SER

IAL_

19

SER

IAL_

010

GN

D11

VD

D12

NC

13

N14

N15

L16

L17

GN

D18

GN

D19

GN

D20

GN

D21

GN

D22

GN

D23

GN

D24

GN

D25

U7

Gre

enPH

Y m

ini s

tam

p

GN

D

3.3V

GN

D

GN

D

SPI-

MO

SI

SPI-

MIS

OSP

I-C

LK

SPI-

CLK

SPI-

MIS

OSP

I-M

OSI

SPI-

CS

SPI-

CS

Gre

enP-

R

Gre

enP-

R

Gre

enP-

I

Gre

enP-

I

Gre

enP-

GPI

O0

Gre

enP-

GPI

O1

Gre

enP-

GPI

O2

Gre

enP-

GPI

O0

Gre

enP-

GPI

O1

Gre

enP-

GPI

O2

100n

F

C39

100n

FC

30

1KR37

1KR36

4.7K

R14

4.7K

R10

2.2K

R12

10K

R8

10K

R9

10K

R31

10K

R32

10K

R35

10K

R33

1 2

CA

N1

2.2u

FC

32

47nF

C49

LED

1LE

D2

LED

3

REG

OU

T10

K

R17

62R47

NC

1

RES

ET2

GN

D3

GN

D4

NC

5

MV

cc6

PG6

7

XO

SCEN

8

Vcc

_CO

RE

9

Vcc

10

Vcc

_IO

11

RX

D12

TXD

13

RTS

14

CTS

15

OP3

16

GN

D17

GN

D18

PG7

19

Aud

io20

Aud

io21

Aud

io22

Aud

io23

GN

D24

OP5

25O

P4/P

G4

2632

K+

2732

K-

28G

ND

29G

ND

30G

ND

31G

ND

32N

C33

NC

34N

C35

NC

36N

C37

NC

38N

C39

NC

40U

5

LMX

9838

Blu

etoo

th m

odul

e

GN

D

GN

D

GN

D

GN

D

3.3V

3.3V

GN

D

BT-

LED

1

BT-

LED

2

OP3

OP5

OP4

BT-

LED

2

D23

3.3V

330K

R25

BT-

LED

1

D22

3.3V

330K

R23

1KR26

1KR27

1KR28

3.3V

3.3V

3.3V

OP3

OP4

OP5

BT1

SCI-

rece

ive

SCI-

trans

mit

GN

D

Gre

en-P

HY

Drive

GN

D10K

R44

Contactor_out

GN

D

12345678910

P7

3.3V

con

trolle

d

GN

D Gre

en-P

HY

GN

D

3.3V

GN

D

3.3V

GN

D

3.3V

GN

D

3.3V

Pre-charge_out

I_measure

I_m

easu

reC

HG

GN

D

DSG

10K

R42

Key

Sw

itch

Blue

toot

h

Buck

reg

ulat

or 3

.3V

Cur

ent P

CB

conn

ecto

r

123

D30

BA

V99

123

D29

BA

V99

123

D20

BA

V99

123

D24

BA

V99

123

D21

BA

V99

1 2

OP3

1 2

OP4

1 2

OP5

100

R18

100

R24

100

R20

510

R34

510

R45

RTS

CTS

RTS

CTS

3.3V

A1

GN

D2

B3

Y4

Vcc

5C

6U

8

SN74

LVC

1G11

3.3V

GN

D

100K

R39

330K

R38

100K

R41

330K

R40

GN

DG

ND

CH

G_o

utD

SG_o

ut

Con

tact

or

Con

tact

or

Contactor_out

Cha

rge_

sens

e

Key

_sen

seK

ey_s

ense

Driv

e_se

nse

Charge

GN

D10K

R29

GN

D

3.3V

123

D25

BA

V99

510

R30

Cha

rge_

sens

e

AN

D G

ate

3.3n

FC

36

GN

D

Pre-

char

ge_o

utK

ey

Driv

e

Cha

rge

CA

N C

omm

unic

atio

n

Gre

en-P

HY

Com

mun

icat

ion

Stat

us L

EDS

BT S

tatu

s LED

S an

d In

puts

4.7u

FC

43

60 O

hm

L2

60 O

hm

L1

60 O

hmL4

DSG_out CHG_out

G1

S2

D3

U9B

3.3V

10K

R48

3.3V

con

trol

3.3V control

3.3V

con

trolle

d

Cur

rent

mea

sure

on/

off c

ontr

ol

GN

D10K

R49

GN

D

3.3V

123

D26

BA

V99

510

R50

12V

_sen

se

12V

_sen

se

12V

_sen

se_m

easu

re

12V_sense_measure

2.2u

FC

52

22uFC

53

GN

D

GN

D

R68

KR

2110

pFC

34

GN

D

330K

R15

10K

R19

100n

F

C38

GN

D

100V

D27

Dio

de150u

H

L3 Indu

ctor

10uH

L5 Indu

ctor

22uFC

45

GN

D

Boo

t1

VIN

2

EN3

SS/T

R4

CLK

5PW

RG

D6

Vse

nse

7C

OM

P8

GN

D9

PH10

U10

TPS5

4060

2.2u

FC

33

2.2u

FC

40

2.2u

FC

41

2.2u

FC

44

2.2u

FC

48

2.2u

FC

47

PIC3001PIC3002CO

C30

PIC3101PIC3102CO

C31

PIC3201PIC3202CO

C32

PIC3301PIC3302CO

C33

PIC3401 PIC3402CO

C34

PIC3501PIC3502CO

C35

PIC3601 PIC3602CO

C36

PIC3

701

PIC3

702COC3

7

PIC3

801

PIC3

802

COC3

8

PIC3

901

PIC3

902

COC3

9

PIC4001PIC4002CO

C40

PIC4101PIC4102CO

C41

PIC4201PIC4202CO

C42

PIC4301PIC4302CO

C43

PIC4401PIC4402CO

C44

PIC4501PIC4502CO

C45

PIC4701PIC4702CO

C47

PIC4801PIC4802CO

C48

PIC4901 PIC4902CO

C49

PIC5201PIC5202CO

C52

PIC5301PIC5302CO

C53

PICAN101

PICAN102

COCAN1

PID2001

PID2002

PID2

003CO

D20

PID2101

PID2102

PID2

103CO

D21

PID2201PID2202CO

D22

PID2301PID2302CO

D23

PID2

401

PID2

402

PID2

403COD2

4

PID2

501

PID2

502

PID2

503CO

D25

PID2

601

PID2

602

PID2

603CO

D26

PID2701PID2702CO

D27

PID2901

PID2902

PID2

903CO

D29

PID3001

PID3002

PID3

003CO

D30

PIL101

PIL1

02

COL1

PIL2

01PIL202

COL2

PIL301

PIL3

02

COL3

PIL401PIL402 COL4

PIL5

01PIL502

COL5

PILED101PILED102COLED1



PIOP

301

PIOP

302 CO

OP3

PIOP

401

PIOP

402 CO

OP4

PIOP501

PIOP502 CO

OP5

PIP601

PIP602

PIP603

PIP604

PIP605

PIP606

PIP607

PIP608

PIP609

PIP6010

PIP6011

PIP6

012

PIP6013

PIP6

014

COP6

PIP701

PIP702

PIP703

PIP704

PIP705

PIP706

PIP707

PIP708

PIP709

PIP7

010

COP7

PIR801PIR802 COR8

PIR901PIR902 COR9

PIR1001PIR1002 COR1

0

PIR1101PIR1102CO

R11

PIR1201PIR1202 COR1

2

PIR1301PIR1302 COR1

3

PIR1401PIR1402 COR1

4PIR1501PIR1502 COR1

5

PIR1601PIR1602 COR1

6

PIR1

701

PIR1

702COR1

7

PIR1

801

PIR1

802COR1

8

PIR1901PIR1902 COR1

9

PIR2

001

PIR2

002COR2

0

PIR2101PIR2102CO

R21

PIR2201PIR2202 COR2

2

PIR2301PIR2302 COR2

3

PIR2

401

PIR2

402COR2

4

PIR2501PIR2502 COR2

5

PIR2601PIR2602CO

R26

PIR2701PIR2702CO

R27

PIR2801PIR2802 COR2

8

PIR2901PIR2902CO

R29

PIR3001PIR3002CO

R30

PIR3101PIR3102CO

R31

PIR3201PIR3202CO

R32

PIR3301PIR3302 COR3

3

PIR3401PIR3402 COR3

4

PIR3501PIR3502CO

R35

PIR3601PIR3602 COR3

6

PIR3701PIR3702 COR3

7

PIR3801PIR3802 COR3

8

PIR3901PIR3902 COR3

9

PIR4001PIR4002CO

R40

PIR4101PIR4102CO

R41PIR4201PIR4202 CO

R42

PIR4301PIR4302 COR4

3

PIR4401PIR4402 COR4

4

PIR4501PIR4502 COR4

5

PIR4601PIR4602CO

R46

PIR4701PIR4702CO

R47

PIR4

801

PIR4

802COR4

8

PIR4901PIR4902CO

R49

PIR5001PIR5002CO

R50

PIU301

PIU302

PIU303

PIU304

PIU305

PIU306

PIU307

PIU308

PIU309

PIU3010

PIU3011

PIU3012

PIU3013

PIU3014

PIU3015

PIU3016

PIU3017

PIU3018

PIU3019

PIU3020

PIU3021

PIU3022

PIU3023

PIU3024

PIU3025

PIU3026

PIU3027

PIU3028

PIU3029

PIU3030

PIU3031

PIU3032

PIU3033

PIU3034

PIU3035

PIU3036

PIU3037

PIU3038

PIU3039

PIU3040

PIU3041

PIU3042

PIU3043

PIU3044

PIU3045

PIU3046

PIU3047

PIU3048

PIU3049

PIU3050

PIU3051

PIU3052

PIU3053

PIU3054

PIU3055

PIU3056

PIU3057

PIU3058

PIU3059

PIU3060

PIU3061

PIU3062

PIU3063

PIU3064

PIU3065

PIU3066

PIU3067

PIU3068

PIU3069

PIU3070

PIU3071

PIU3072

PIU3073

PIU3074

PIU3075

PIU3076

PIU3077

PIU3078

PIU3079

PIU3080

COU3

PIU501

PIU502

PIU503

PIU504

PIU505

PIU506

PIU507

PIU508

PIU509

PIU5010

PIU5011

PIU5012

PIU5013

PIU5014

PIU5015

PIU5016

PIU5017

PIU5018

PIU5019

PIU5020

PIU5021

PIU5022

PIU5023

PIU5024

PIU5025

PIU5026

PIU5027

PIU5028

PIU5029

PIU5030

PIU5031

PIU5032

PIU5033

PIU5034

PIU5035

PIU5036

PIU5037

PIU5038

PIU5039

PIU5040

COU5

PIU601

PIU602

PIU603

PIU604

PIU605

PIU606

PIU607

PIU608

COU6

PIU701

PIU702

PIU703

PIU704

PIU705

PIU706

PIU707

PIU708

PIU709

PIU7010

PIU7011

PIU7012

PIU7013

PIU7014

PIU7015

PIU7016

PIU7017

PIU7018

PIU7019

PIU7020

PIU7021

PIU7022

PIU7023

PIU7024

PIU7025

COU7

PIU801

PIU802

PIU803

PIU804

PIU805

PIU806

COU8

PIU901

PIU902

PIU903

COU9

B

PIU1001

PIU1002

PIU1003

PIU1004

PIU1005

PIU1006

PIU1007

PIU1008

PIU1009

PIU10010

COU1

0

PIC4302

PIC4401

PIC4502

PIC4702

PIC4801

PID2002

PID2102

PID2

402

PID2

502

PID2

602

PID2902

PID3002

PIL101

PIL2

01

PIL401

PIL502

PIP605

PIR802 PIR901 PIR1001PIR1401

PIR2302PIR2502

PIR2602PIR2702

PIR2802

PIU506

PIU5010

PIU5011

PIU603

PIU7012

PIU805

PIU902

PIR4

801

PIU3045

NL303V controlPIP704

PIU903

PIP703

PIR4902NL12

V0se

nse PI

D260

3PIR4901 PIR5002

PIU3027

NL12V0sense0measure

PIU3039

PIC3102PIC5202

PIR1502PIU1002

PIU3066

PIU3050

PIU502

NLBT

1

PID2201

PIU507

NLBT

0LED

1

PID2301

PIU5019

NLBT

0LED

2

PIR4301

PIU3031

NLCAN0

EN

PIR3701

PIU3033

NLCANRx

PIR3602

PIU3032

NLCANTx

PID2

503

PIR2901 PIR3002

PIU3075

NLCharge

PIP707

PIR2902NLCh

arge0sen

se

PIR3802 PIR3801 PIR3902

PIU803

NLCHG0out

PIU3078

PIU806NL

Cont

acto

r

PID2

003

PIR2

001

PIU804

NLContactor0out

PIU3077

PIU5015

NLCT

S

PID3

003

PIR4401 PIR4502

PIU3055

NLDrive

PIP708

PIR4402NLDrive0sense

PIR4002 PIR4001 PIR4102

PIU801

NLDSG0out

PIC3002

PIC3101

PIC3201 PIC3301

PIC3402

PIC3501

PIC3602

PIC3

701

PIC3

902

PIC4001

PIC4101

PIC4201

PIC4301

PIC4402

PIC4501

PIC4701

PIC4802

PIC4902

PIC5201

PIC5301

PID2001

PID2101

PID2

401

PID2

501

PID2

601

PID2701

PID2901

PID3001

PILED101PILED201

PILED301

PIP604

PIP606

PIP608

PIP6010

PIP6

012

PIP701

PIR1101

PIR1302

PIR1601

PIR1901

PIR3001PIR3401

PIR3901PIR4101

PIR4202

PIR4501PIR5001

PIU308

PIU3021

PIU3022

PIU3035

PIU3052

PIU3053

PIU3071

PIU3073

PIU503

PIU504

PIU5017

PIU5018

PIU5024

PIU5027

PIU5029

PIU5030

PIU5031

PIU5032

PIU602

PIU704

PIU7011

PIU7014

PIU7015

PIU7018

PIU7019

PIU7020

PIU7021

PIU7022

PIU7023

PIU7024

PIU7025

PIU802

PIU1009

PIP7

010

PIU7016

PIU7017

NLGreen0PHY

PIU301

PIU701

NLGr

eenP

0GPI

O0

PIU302

PIU702

NLGr

eenP

0GPI

O1

PIU303

PIU703

NLGr

eenP

0GPI

O2

PIU304

PIU7010

NLGreenP0I

PIU305

PIU705

NLGreenP0R

PIC3601PI

D210

3PI

R180

1

PIU3026

NLI0measurePI

D290

3PIR3301 PIR3402

PIU3080

NLKey

PIP709

PIR3302NLKey0sense

PIR3102

PIU3062

NLLED1

PIR3202

PIU3063

NLLED2

PIR3502

PIU3064

NLLED3

PIC3001PI

L102

PIU3036

PIC3202PIU307

PIC3302PIL202

PIR1201

PIU3020

PIC3401PIR2102

PIU1008

PIC3502

PIU1004

PIC3

702

PIR2101

PIC3

801

PIU1001

PIC3

802

PID2702PIL301

PIU10010

PIC3

901

PIL402PIU3070

PIC4002PIU3054

PIC4102PIU3072

PIC4202PIC5302

PIL3

02PI

L501

PIR2202

PIC4901PIR4601 PIR4702

PICAN101

PIR4602

PIU607

PICAN102

PIR4701

PIU606

PID2202PIR2301

PID2302PIR2501PILED102PIR3101

PILED202PIR3201

PILED302PIR3501

PIOP

301

PIR2601

PIOP

401

PIR2701

PIOP501

PIR2801

PIP6013

PIR1002PI

P601

4

PIR1402

PIP702

PIR1

802

PIP705

PIR2

402

PIP706

PIR2

002

PIR1102PIU1005

PIR1202PIU309

PIR1501 PIR1602

PIU1003

PIR1

701PIU3037

PIR1902PIR2201

PIU1007

PIR3601PIU601

PIR3702PIU604

PIR4201PIU608 PIR4302

PIU605

PIR4

802

PIU901

PIU306

PIU3011

PIU3012

PIU3013

PIU3014

PIU3015

PIU3016

PIU3017

PIU3018

PIU3019

PIU3023

PIU3024

PIU3025

PIU3028

PIU3029

PIU3030

PIU3038

PIU3044

PIU3048

PIU3051

PIU3056

PIU3061

PIU3065

PIU3067

PIU3068

PIU3069

PIU3074

PIU501

PIU505

PIU508

PIU509

PIU5020

PIU5021

PIU5022

PIU5023

PIU5028

PIU5033

PIU5034

PIU5035

PIU5036

PIU5037

PIU5038

PIU5039

PIU5040

PIU7013

PIU1006

PIOP

302

PIU5016

NLOP

3

PIOP

402

PIU5026NL

OP4

PIOP502

PIU5025NLO

P5

PID2

403

PIR2

401

PIU3079

NLPre0charge0out

PIR1

702

PIU3076

PIU5014

NLRT

S

PIU3049

PIU5013

NLSC

I0re

ceiv

e

PIU3047

PIU5012

NLSC

I0tr

ansm

it

PIR801PIU3034

PIR902PIU3040

PIU3041

PIU709

NLSPI0CLK

PIU3043

PIU708

NLSPI0CS

PIU3042

PIU707

NLSP

I0MI

SO

PIU3046

PIU706

NLSP

I0MO

SI

PIP609

PIP6011

PIU3057NL

TCK

PIP603

PIU3059NL

TDI

PIP607

PIU3058NL

TDO

PIP601

PIU3060NL

TMS

PIP602

PIR1301

PIU3010

NLTRST



11

22

33

44

55

66

DD

CC

BB

AA

2

Mel

lowC

abs

Fran

shoe

kSo

uth

Afric

a

7

BMS

21.

120

16/1

1/22

09:5

7:09

PM

C:\U

sers

\166

8781

7\D

ropb

ox\M

eest

ers\

PCB

des

igns

\PC

B 1

.1\P

WR

524A

_FET

s.Sch

Doc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:Ta

bloi

dB

arth

o H

orn

Engi

neer

:

BA

TT-

BA

TT-

GN

D

C0

BA

TT+

SRP

SRN

-

0 - 6

0V

Inpu

t

+

10µF

C50

1SM

A54

AT3

G

D31

10µF

C51

PIC5001 PIC5002CO

C50

PIC5101 PIC5102CO

C51

PID3101PID3102CO

D31

PIC5001PID3102

PIC5102PID3101

NLBATT0

PIC5002 PIC5101



C.1.2 Balance Schematic

11

22

33

44

DD

CC

BB

AA

*

* * * * **

Bala

nce

circ

uit *

*20

16/1

1/22

09:1

8:05

PM

C:\U

sers

\166

8781

7\D

ropb

ox\M

eest

ers\

PCB

des

igns

\Bal

ance

PCB

\Bal

ance

PCB

.Sch

Doc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:A

4En

gine

er:

B. H

orn

F1 SSQ

123

D1

BA

V99

dio

de

0.8K

R1

Res

3

10K

R2

Res

3V

sens

e/nB

alan

ce

CEL

L-G

ND

CEL

L++ -

t°

RT1

NTC

10K

1%

Bla

nce_

Res

isto

rGen

eric

boa

rd

P1 Pow

er

1 2

P6 2 w

ay 2

.54m

m m

olex

SM4T

6V7A

YD

2G

1

S2

D3

U3B

P5 GN

D

P3 Mea

sure

PID101

PID102

PID103COD

1

PID201PID202COD2

PIF101

PIF102COF

1

PIP101COP

1

PIP301COP

3

PIP501COP

5

PIP601

PIP602COP

6

PIR101

PIR102

COR1

PIR201

PIR202

COR2

PIRT101

PIRT102CO

RT1

PIU301

PIU302

PIU303

COU3B

PIF102

NL0

PID101

PID201

PIP501

PIU303

NL0

NLBlance0Res

istor

PID102

PID202

PIF101

PIP101

PIR101

PIU302

PID103

PIR201

PIU301

PIP601

PIRT102

PIP602

PIRT101

PIP301

PIR102

PIR202

NLVs

ense

0nBa

lanc

e

Figure C.1: Balance schematic



C.1.3 Current Sense Schematic

11

22

33

44

DD

CC

BB

AA

1

* * * * *1

Cur

rent

Sen

sor *

3.1

2016

/11/

2109

:24:

34 P

MC

:\Use

rs\1

6687

817\

Dro

pbox

\Mee

ster

s\PC

B d

esig

ns\C

urre

nt3.

1 pc

b\C

urre

ntPc

b.Sc

hDoc

Title

Size

:N

umbe

r:

Dat

e:Fi

le:

Rev

isio

n:

Shee

tof

Tim

e:A

4En

gine

er:

B H

orn

Gre

enPH

Y

GN

D

Batte

ry P

lus

Hig

h cu

rren

t

Vcc

1

GN

D2

VIO

UT

3

IP+ 4IP-5

U2

AC

S758

Cur

rent

sens

e

12345678910

P2

3.3V

I_m

easu

re

GN

D

3.3V

I_m

easu

re

GN

D

3.3V

123

D4

BA

V99

11

H2

M6

hole

1 1

H1

M6

hole

SMA

J54C

AD

3+

1

-2

Load

3

Load

4U

1

CPC

1008

N R

elay

NO

GN

D

GN

D

123

D6

BA

V99

3.3V

Pre_Charge

3.3V

470

R3

G1

S2

D3

Q1B

SMA

J54C

AD

1

G1

S2

D3

Q2B

GN

D

10nF

C2

Main_Contact

GN

D

123

D5

BA

V99

3.3V

Pre_ChargeMain_Contact

Key

_sen

seD

rive_

sens

eC

harg

e_se

nse

Key

_sen

seD

rive_

sens

eC

harg

e_se

nse

Mai

n_C

onta

ct_o

ut

Pre_

Cha

rge_

out

Pre_

Cha

rge_

out

Mai

n_C

onta

ct_o

ut

48V

(5A

fuse

d)

48V

(5A

fuse

d)

GreenPHY

Batte

ry O

utpu

t

In connectorOut connector

Mai

n C

onta

ctor

Dri

ver

Pre-

char

ge R

esist

or D

rive

r

Con

tact

or w

ord

gedr

yf d

eur d

ie 4

8V(5

A fu

se).

Mai

n_C

onta

ct_o

ut w

ord

gebr

uik

as d

ie sk

akel

aar.

F1 5 A

D2

SB51

00-T

100

R8

100

R6

100

R7

1MR4

1MR2

10K

R1

3KR5

Bat

t_ou

t

Bat

t_ou

t

10m

H

L1 Indu

ctor

1234567

P1 7 w

ay 2

.54m

m m

olex

12V

_sen

se

12V

_sen

se

2.2u

FC

1

2.2u

FC

3

10K

R9

PIC101PIC102COC

1

PIC201 PIC202COC

2

PIC301PIC302COC

3

PID101PID102COD

1

PID201

PID202CO

D2

PID301PID302COD

3

PID401

PID402

PID403

COD4

PID501

PID502

PID503COD

5

PID601

PID602

PID603COD

6

PIF101

PIF102

COF1

PIH101

COH1 PIH201

COH2

PIL101

PIL102

COL1

PIP101

PIP102

PIP103

PIP104

PIP105

PIP106

PIP107

COP1

PIP201

PIP202

PIP203

PIP204

PIP205

PIP206

PIP207

PIP208

PIP209

PIP2

010

COP2

PIQ101

PIQ102

PIQ103

COQ1B

PIQ201

PIQ202

PIQ203

COQ2B

PIR101

PIR102COR

1

PIR201PIR202 COR2

PIR301

PIR302

COR3

PIR401PIR402 COR4

PIR501 PIR502COR5

PIR601 PIR602

COR6

PIR701

PIR702

COR7

PIR801

PIR802COR

8

PIR901

PIR902CO

R9

PIU101

PIU102

PIU103

PIU104

COU1

PIU201

PIU202

PIU203

PIU204PIU205COU2

PIC102

PIC302

PID402

PID502

PID602

PIL102PIU101

PIU201

PIP101

PIP203NL

12V0sense

PID202

PIP107

NL48

V(5A

fus

ed)

PID301

PIF102

PIH101PIU103

PIU205NLBatt0out

PIP106

PIP207NL

Char

ge0s

ense

PIP105

PIP208NL

Driv

e0se

nse

PIC101

PIC201

PIC301

PID101

PID401

PID501

PID601

PIP201

PIQ102

PIQ202

PIR201

PIR401

PIU202

PID201

PIF101

PIP2

010

NLGreenPHY

PIP202

PIR601NLI0measure

PIP104

PIP209NL

Key0sense

PID503

PIR102

PIR801

NLMain0Contact

PID102

PIP103

PIQ103

NLMain0Contact0out

PIC202PID403PIR602

PIR701

PID302PIR301

PIU104

PIH201PIU204

PIL101

PIP204

PIP205

PIR902

PIP206

PIR802

PIQ101

PIR101 PIR202

PIQ203 PIR501 PIR502

PIU102

PIR702PIU203

PID603

PIQ201

PIR402

PIR901

NLPre0Charge

PIP102

PIR302

NLPre0Charge0out

Figure C.2: Current sense schematic
