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Distributed Control, iEMS, and On-line Battery Modeling on FREEDM SystemsBattery Modeling on FREEDM Systems
Mo-Yuen Chow, Ph.DDepartment of Electrical and Computer Engineering
North Carolina State UniversityyRaleigh, North Carolina
OverviewBrief descriptions of our FREEDM and ATEC projects
• Time-Sensitive Distributed Controls on FREEDM Systems (withTime Sensitive Distributed Controls on FREEDM Systems (with Ziang Zhang)
• Intelligent Energy Management System (iEMS) for PHEVs in Municipal Parking Deck (with Wencong Su)Municipal Parking Deck (with Wencong Su)
• Comprehensive Online Dynamic Battery Modeling for PHEV Applications (with Hanlei Zhang)P iti th FREEDM h d• Positions on the FREEDM research roadmaps
Consensus algorithms for a time-sensitive distributed controlled FREEDM Systemcontrolled FREEDM System
• Graph theory• Convergence rate• Time delay
Summary and future works
Future Renewable Electric Energy Delivery and Management Systems Center
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Our current FREEDM and ATEC projects
TimeTime sensitive distributed controls on FREEDMsensitive distributed controls on FREEDM
projects
TimeTime--sensitive distributed controls on FREEDM sensitive distributed controls on FREEDM SystemsSystems• Ziang Zhang• Xichun YingXichun Ying
Intelligent Energy Management System for PHEVs at A Intelligent Energy Management System for PHEVs at A Municipal Parking DeckMunicipal Parking Deck• Wencong Su• Xu Yang
H l i Zh• Hanlei Zhang
Comprehensive Online Dynamic Battery Modeling for Comprehensive Online Dynamic Battery Modeling for PHEV ApplicationsPHEV ApplicationsPHEV Applications PHEV Applications • Hanlei Zhang• Wencong Su
Future Renewable Electric Energy Delivery and Management Systems Center
The interactions among the three projectsthree projects
iEMS
Power allocationBattery Model
Real-Time Monitoring and ControlEmbedded
DAQ
Future Renewable Electric Energy Delivery and Management Systems Center
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gDAQDAQSoC, SoH, SoF
Digital Testbed: IEM Demonstration Grid connected•Dynamic pricing•Renewable/storage/loadDi it l T tb d
•Power sharingIslandingRenewable/storage/load Digital Testbed
& Demonstration•Frequency regulation
hnol
ogy
Y2.E.C5
Y2.E.C8 SCADASystem
ImprovedSCADASystem
Y2.E.C4
ablin
g Te
c
SST Prototype Deliver three pieces
Gen-IISST design
Y2.E.C6
nce Y2.F.C3
Ena Y2.F.C1 Simulink
ModelRTDSModel
Additional Controls
Distributed O i i dOptimized
enta
l Sci
en
Y2.F.C515kV SiC
DistributedControl
Framework
Optimized Control
pSystemDesignY2.F.C4
Fund
ame
Y2.F.C8
15kV SiCMOSFET
/Diode
Packageddevice
Y2 F C9 11600V GaNMOSFET
Packageddevice
Future Renewable Electric Energy Delivery and Management Systems CenterYEAR 3YEAR 2Sept.’09 Sept.’ 10 Sept.’11
Y2.F.C9-11 MOSFET device
PHEV TestbedMotor Drive TestEnergy ManagementOptimized SupplyY2.D.C1 PHEV Vehicle
PHEV Roadway Testetc.Y2.D.C2
TestBed
Y2.D.C3
Digital Testbed
PHEVParking Deck
EnergyManagement
hnol
ogy
Y2.E.C7 DESD
ablin
g Te
c
ChargerY2.E.C9
Y2 E C10DESD
Y2.E.C1
Ena Y2.E.C10
nce
Battery ModelBattery
Equivalent CircuitModel
Y2.F.C7 Capacity Fade Mechanismen
tal S
cien
Y2.F.C6High-Power Long-Life Batteries
Optimized BatteryCellsFu
ndam
e
Future Renewable Electric Energy Delivery and Management Systems CenterYEAR 3YEAR 2Sept.’09 Sept.’ 10 Sept.’11
FREEDM System: a very smart Smart Gridsmart Smart Grid
Goal of Smart Grid: Intelligent power delivery with optimal efficiency,effectiveness, power quality, resilience, reliably, availability, etc.
Features• Self healing property• Delivery of high power quality• Delivery of high power quality• Customized power usages• Effective and efficient energyy
systems• Immunity to cyber attack
224/7 il bilit• 224/7 availability• Seven sigma reliability• …
Future Renewable Electric Energy Delivery and Management Systems Center
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Time-sensitive distributed networked control systems (TS D-NCS)control systems (TS D NCS)
Enabling and enpowering individuals and small groups of sensors, actuators and controllers go global easily andcontrollers go global easily and seamlessly.Unique character – the newfound power for individuals (sensors, actuators,
t ll ) t ll b t / tcontrollers) to collaborate/cooperate globally to solve local challenging problems (that cannot be solved otherwise)Provide optimized system performance with low cost through distributed information utilizationsEnable real time monitoring control andEnable real-time monitoring, control and operation globally with distributed local informationCould usher in an amazing era of
it i ti d ll b tiprosperity, innovation, and collaboration, by integrating distributed sensors, actuators, and controllers around the world.
Future Renewable Electric Energy Delivery and Management Systems Center
Central Control vs. Distributed Control
Puppet
vs.
School of fish
Central Control Distributed Control [1]
System Puppets and Puppeteer School of fish
Controller Puppeteer (Single) Fish (Multiple)
Information available to the
Puppeteer know the position of every part of puppet
Each fish only know the position of neighbors (Local)
controllery p p pp
(Global)g ( )
Control Goal Keep certain pattern of style and moving around
Keep certain pattern of shape and moving around
Future Renewable Electric Energy Delivery and Management Systems Center
9• Iain D. Couzin, Jens Krause, Nigel R. Franks and Simon A. Levin, “Effective leadership and decision-making in
animal groups on the move”, Nature 433, 513-516 (3 February 2005)• …
Central control vs. distributed control -2
Central Controlled System Distributed Controlled System
P C t l l ith i l ti l R li d th t ti l b d fPros • Control algorithm is relatively simple
• …
• Relieved the computational burden for a single controller
• Ease of heavy data exchange demand• Single point of failure will notSingle point of failure will not
necessarily affect the others• Controllers do not need the entire
system state information• …
Cons • Computational limitation of central controller
• Only part of the system states are available to each distributed controller
• Communication limitation of central controller
• Single point of failure will affect the entire system
• Normally need complex algorithms and designs
• …the entire system
• …Usages Normally more appropriate for
systems with simple controlNormally more appropriate for large-scale systems need sophisticated control
Future Renewable Electric Energy Delivery and Management Systems Center
systems with simple control systems need sophisticated control
10
Time-sensitive distributed network control systems challengescontrol systems challenges
Time-sensitive applications/ Time delay issuesU
U
Hard real-timeSoft real-time
Hard real-time control
Soft real-time control Umax
U( )
U: utility functionT : hard deadline: actual time Non real-time control
Resource constraints (e g bandwidthUmin
tT
Resource constraints (e.g., bandwidth,
generation)/ resources allocation issues
Data-sensitive applications/ Security issues
…
- M.-Y. Chow, S. Chiaverini, and C. Kitts, "Guest Editorial on Focused Section on Mechatronics in Multi Robot Systems," IEEE Transactions on Mechatronics, vol. 14, pp. 133-140, April 2009, pp. 133-140.
- R A Gupta and M -Y Chow "Networked Control Systems: Overview and Research Trends " forth coming accepted
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- R. A. Gupta and M.-Y. Chow, Networked Control Systems: Overview and Research Trends, forth coming, accepted for publication in IEEE Transactions on Industrial Electronics, October 2009.
- …
Time-sensitive Distributed Controls on FREEDM SystemsSyste s
RA: Ziang Zhang (John)
Phase I : Consensus Algorithms g g ( )
Department of Electrical and Computer EngineeringNorth Carolina State University
Raleigh, North Carolina
Future Renewable Electric Energy Delivery and Management Systems Center
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What is consensus?
ConsensusConsensus [1]
A school of fish
Goal: swimming towards oneChorus
ConsensusConsensus
Goal: swimming towards one same direction Goal: Synchronize the melody
[1] Larissa Conradt and Timothy J Roper “Consensus decision making in animals” Trends in Ecology & Evolution Volume
Future Renewable Electric Energy Delivery and Management Systems Center
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[1]. Larissa Conradt and Timothy J. Roper, Consensus decision making in animals , Trends in Ecology & Evolution, Volume 20, Issue 8, August 2005, Pages 449-456.
How can consensus be reached?
Consensus Network
Independent Physical SystemsEach of them follow their own dynamic
A sufficient condition for reach consensus: If there is a directed spanning tree* exists in the communication network, then consensus can be reached. [1]
*Spanning tree: a minimal set of edges that connect all nodes
Future Renewable Electric Energy Delivery and Management Systems Center
14[1] Wei Ren Randal W. Beard Ella M. Atkins , “A Survey of Consensus Problems in Multi-agent Coordination”, 2005 American Control Conference June, 2005. Portland, OR, USA
Consensus algorithm based modeling
Adjacency matrix of a finite graph G on n vertices is the n × n matrix where the entry aij is the number of edges from vertex i to vertex j aij =0 representthe entry aij is the number of edges from vertex i to vertex j, aij =0 represent that agent i cannot receive information from agent j
C bl d li0 1 1⎡ ⎤⎢ ⎥
Example:
iξ, 1,...,i i i nξ ξ= =
Consensus problem modelingLocal information stateFirst-order system
1 0 11 1 0
A ⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦
Adj t iConsensus algorithm:
Scalar From Matrix Form
i i Adjacency matrix
Continuous
Discrete1
( ), 1,...,n
i ij i jj
a i nξ ξ ξ=
= − − =∑ nLξ ξ= −
[ 1] [ ] 1n
k d k iξ ξ+ ∑
Where Ln is the Laplacian matrix associated with A,
Discrete[ 1] [ ]nk D kξ ξ+ =
1
[ 1] [ ], 1,...,i ij jj
k d k i nξ ξ=
+ = =∑
Future Renewable Electric Energy Delivery and Management Systems Center
n p ,and Dn is Row-stochastic matrix associated with A.
15
Consensus algorithm performance
2Continuous-time Consensus
Consensus performance with different network topology - Step inputs as load references
0 5
1
1.5
k)
4 -1 -1 -1 -1 -1 1 0 0 0 -1 0 1 0 0
1 0 0 1 0L
⎡ ⎤⎢ ⎥⎢ ⎥
= ⎢ ⎥⎢ ⎥
p
-0.5
0
0.5ξ(k
SST1SST2SST3SST4SST5ref
-1 0 0 1 0 -1 0 0 0 1
⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦
Al b i ti it λ 10 5 10 15 20 25 30 35 40
-1
k
1.5
2Continuous-time Consensus
2 -1 -1 0 0⎡ ⎤⎢ ⎥
Algebraic connectivity λ2 = 1
0.5
1
ξ(k)
SST1
-1 1 0 0 0-1 -1 2 0 00 0 -1 2 -10 0 0 -1 1
L
⎡ ⎤⎢ ⎥⎢ ⎥
= ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦
0 5 10 15 20 25 30 35 40-1
-0.5
0
SST1SST2SST3SST4SST5ref
⎣ ⎦
Algebraic connectivity λ2 ≈ 0.38
Al b i ti it λ Th l b i ti it f
Future Renewable Electric Energy Delivery and Management Systems Center 16
0 5 10 15 20 25 30 35 40k
Algebraic connectivity λ2 : The algebraic connectivity of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G.
Consensus algorithm based modeling
Consensus test with time-delay1.5
2Continuous-time Consensus with delay=0
τ3
4Continuous-time Consensus with delay=pi/6
( ) ( )t L tξ ξ τ= − −2 1 1− −⎡ ⎤
⎢ ⎥3
Continuous-time Consensus with delay=0.5
-1.5
-1
-0.5
0
0.5
1
ξ(t)
-2
-1
0
1
2
3
ξ(t)
1 2 11 1 2
L ⎢ ⎥= − −⎢ ⎥− −⎢ ⎥⎣ ⎦
-1
0
1
2
ξ(t)
0 5 10 15 20-3
-2.5
-2
t 0τ =0 5 10 15 20
-4
-3
t
Continuous-time Consensus with delay=0 7854
0.5236sec2 n
πτλ
= ≈2 33, 3nλ λ λ= = =
1 1 0−⎡ ⎤
0 5 10 15 20-3
-2
t
0.5secτ =Continuous-time Consensus with delay=0 5
-0.5
0
0.5
1
1.5
2Continuous-time Consensus with delay=0
ξ(t) -1
0
1
2Continuous time Consensus with delay=0.7854
ξ(t)
1 1 01 1 0
0 1 1L
⎡ ⎤⎢ ⎥= −⎢ ⎥
−⎢ ⎥⎣ ⎦1
-0.5
0
0.5
1
1.5
2Continuous time Consensus with delay=0.5
ξ(t)
0 5 10 15 20-3
-2.5
-2
-1.5
-1
t
0τ =0 5 10 15 20
-4
-3
-2
t
0 7854secπτ = ≈2 1, 2λ λ= = 0 5secτ =0 5 10 15 20
-3
-2.5
-2
-1.5
-1
t
0τ = 0.7854sec2 n
τλ
= ≈2 1, 2nλ λAlgebraic Connectivity
Largest
2 :λ
:λ
A sufficient condition for convergence [1] of the consensus algorithm above is that
2 n
πτλ
<
0.5secτ =
Future Renewable Electric Energy Delivery and Management Systems Center 17
Largest eigenvalue of L
:nλ[1] Reza Olfati-Saber and Richard M. Murray, “Consensus Problems in Networks of Agents
With Switching Topology and Time-Delays”, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER 2004
Current work: Application of consensus algorithms on FREEDM systemsalgorithms on FREEDM systems
Formulate the consensus algorithms for the FREEDM systems with both continuous time models and discrete event modelstime models and discrete event modelsDesign high performance and reliable consensus algorithms for FREEDM systemsInteracting with other groups
NCSU (communication network resilience, delay, reconfiguration, NCSU green hub models,NCSU (communication network resilience, delay, reconfiguration, NCSU green hub models, distributed control algorithms – Dr. Mueller, Dr. Jiang, Dr. Baran)MST (MST FREEDM testbed, load balancing algorithms – Dr. McMillin, Dr. Crow)ASU (SST models, optimization – will establish closer interactions)
Future Renewable Electric Energy Delivery and Management Systems Center
18Publication: Ziang Zhang, Mo-Yuen Chow, “Consensus Algorithms for a Distributed Controlled FREEDM System”, FREEDM Annual Conference, May 2010
Some future works of the projects
Time-Sensitive Distributed Controls on FREEDM SystemsDevelop consensus algorithms under separated power network and communication network
projects
gDevelop other distributed control algorithmsAnalyze and develop distributed controls to handle time-delayAnalyze and develop adaptive sampling strategies and distributed bandwidth allocation algorithms to handle bandwidth limitationalgorithms to handle bandwidth limitationCollaborate with other FREEDM teams to demonstrate the distributed control algorithms on the FREEDM testbeds…
Intelligent Energy Management System (iEMS) for PHEVs in Municipal Parking Deck
Integrate the comprehensive battery models from the Battery Monitoring System Development and Deployment project into the iEMS
Interact with the Distributed Control of FREEDM system project on using some ofthe developed distributed control algorithms for iEMS
Collaborate with the Bi-directional electric vehicle supply equipment project toCollaborate with the Bi directional electric vehicle supply equipment project toimplement distributed control algorithms
Use the architect developed by Optimization of the power delivery architectureproject to develop related communication network requirements and control
i t / t i t
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requirements/constraints
Demonstrate iEMS algorithm on the PHEV testbed
…19
Some future works of the projects – contprojects cont.
Comprehensive Online Dynamic Battery Modeling for PHEV ApplicationsDevelop online model parameter identification algorithms to properly identify theDevelop online model parameter identification algorithms to properly identify the model characteristic parameters in real timeDesign appropriate State of Charge (SoC), State of Health (SoH), and State of Function (SoF) measures to infer proper battery performances serving the iEMS( ) p p y p gand other FREEDM projectsCollaborate with other ATEC teams to implement the algorithms on the actual PHEV testbedCollaborate with the distributed control group to provide proper battery bank real-time models…
Continue and expand interactions with other FREEDM and ATEC teamsEnhance the interactions with industrial partnerspReach out to new companies…
Future Renewable Electric Energy Delivery and Management Systems Center
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Acknowledgements: These works were partially supported by the National Science Foundation (NSF) under Award Number ( )EEC-08212121.
Future Renewable Electric Energy Delivery and Management Systems Center
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Comprehensive Online Dynamic Battery Modeling
Objective• Develop models and algorithms that can adapt to different types of batteries, their actual
conditions and their operating environments based on the in situ measurements on the batteryconditions, and their operating environments based on the in-situ measurements on the battery.• Design appropriately State of Charge, State of Health and State of Function measures to infer
proper battery performances.
Motivation• Battery states information helps to
enable optimal control over thebattery charging/discharging process,providing essential information for
Battery Monitoring System
providing essential information foriEMS to allocate power optimally.
SoC: State of ChargeSoC: State of Charge
Temperature, humidity, etc.Voltage & currentLithium Ion Battery
Challenges• Battery relaxation effect
B tt h t i ff t
gSoH: State of HealthSoF: State of Function
gSoH: State of HealthSoF: State of Function
Intelligent Energy Management System(iEMS)
Battery ModelBattery Model
• Battery hysteresis effect• Environmental effect, such as
temperature, humidity, etc.• Aging effect
Future Renewable Electric Energy Delivery and Management Systems Center
Related Works in Battery Model Area
Battery Models Pros Cons
Input‐output mapping [1] • Easy to obtain model parameters • Can not reflect system dynamics
EIS‐based model [2]• Can be accurate in finding the model
parameters• Special instrument is required• Battery must be tested offlinep y
Transient response mapping [3] • Predict battery SoC• Reflect the battery dynamics partially
• vt (iL) dynamics modeling need to be improved
• No hysteresis effect considered
Require research on:Dynamic online model [4‐6]
(our model)• Accurate under dynamic load condition• Suitable for online PHEV application
Require research on:• Relaxation and hysteresis effect modeling• Online parameter identification algorithm
development
References[1] O. Tremblay, L. Dessaint and A. Dekkiche, “A generic battery model for the dynamic simulation of hybrid electric vehicles”, in Proceedings of Vehicle Power and
Propulsion Conference, VPPC 2007. IEEE, pp. 284-289.[2] S B ll M Th l R D k d E K d "I d b d i l ti d l f it d Li i b tt i f l t i li ti " IEEE[2] S. Buller, M. Thele, R. Doncker and E. Karden, "Impedance-based simulation models of super-capacitors and Li-ion batteries for power electronic applications," IEEE
Transactions on Industry Applications, vol. 41, 2005, pp. 742-747.[3] M. Chen and G. Rincon-Mora, “Accurate electrical battery model capable of predicting runtime and I-V performance,” IEEE Transactions on Energy Conversion, vol. 21,
2006, pp. 504-511.[4] H. Zhang and M. Chow, "Comprehensive dynamic battery modeling for PHEV applications," in Proceedings of Power & Energy Society General Meeting, IEEE, 2010.[5] H. Zhang and M. Chow, “Comprehensive dynamic battery model serving a municipal parking deck intelligent energy management system (iEMS),” submitted to the
second FREEDM Annual Conference 2010
Future Renewable Electric Energy Delivery and Management Systems Center
second FREEDM Annual Conference, 2010.[6] H. Zhang and M. Chow, “Dynamic battery model including battery relaxation and hysteresis effect for PHEV applications,” submitted to the 36th Annual Conference of
the IEEE Industrial Electronics Society, IEEE IECON10, 2010.
Dynamic battery model including battery relaxation and hysteresis effect
RelaxationRelaxation effecteffect modelingmodeling
• Use 2-norm and infinity norm to quantify the modelaccuracy 1
2n⎛ ⎞
• Use series connected RC parallel circuits to model thebattery relaxation effect
( )
22
21
1 2
max , , ... , .
n
ii
n
e
e e e=
∞
⎛ ⎞= ⎜ ⎟⎝ ⎠
=
∑e
e ( )1 2, , , n∞
Fig. 1. The equivalent circuit of a battery cell.
e2
e
• Heuristically, more RC circuits provides better modelaccuracy
olta
ge (v
)
rren
t (A
)
Vo
Cur
Fig. 3. Modeling error with different RC circuit number.
• On the other hand, more RC circuits also increase model computational complexity
Future Renewable Electric Energy Delivery and Management Systems Center
Fig. 2. Relaxation effect modeling with one RC circuit and two RC circuits. • We need to balance between accuracy and complexity according to the application requirement
Automatic Remote Battery Charge/Discharge Web Based Workstation
• Real time controlled battery charge discharge experimentsElectronic Electronic
L dL d Po er S pplPo er S pplMultimeterMultimeter
• Automatic battery charge discharge with user defined load profile
• Friendly GUI interface to real time battery measurements• Basic platform for online model parameter identification
with in-situ battery measurements
LoadLoad Power SupplyPower Supply
Testing BatteryTesting Battery
with in situ battery measurements
Fig. 1. The Lithium-ion battery cell and the testing instruments.
Fig. 3. Graphic user interface of the battery charge/discharge workstation.Fig. 2. Electrical connection and communication links.
Related publications1. H. Zhang and M. Chow, "Comprehensive dynamic battery modeling for PHEV applications," in Proceedings of Power & Energy Society
General Meeting, IEEE, 2010.2. H. Zhang and M. Chow, “Comprehensive dynamic battery model serving a municipal parking deck intelligent energy management system
Future Renewable Electric Energy Delivery and Management Systems Center
(iEMS),” in Proceedings of the second FREEDM Annual Conference, 2010.3. H. Zhang and M. Chow, “Dynamic battery model including battery relaxation and hysteresis effect for PHEV applications,” submitted to the
36th Annual Conference of the IEEE Industrial Electronics Society, IEEE IECON10, 2010.
Intelligent Energy Management System for PHEVS at a Municipal Parking Deck
•Power Allocated•Rate of charge•Other control messages
User Profile EntryRA: Wencong SuElectrical and Computer EngineeringN th C li St t U i it
a u c pa a g ec
gNorth Carolina State UniversityRaleigh, North Carolina
Objective•Available Power•Pricing
UtilityData Acquisition
(DSP, FPGA etc.)
Objective•To develop an Intelligent Energy ManagementSystem (iEMS) architecture to achieve theoptimal power allocation to PHEVs at a
•Time of availability•Type of charge
Communication Medium:
p pmunicipal parking deck and also allow forVehicle-to-Grid (V2G) technology.
iEMSoptimization
•Type of charge•Pricing preferences•Current state of charge•Power consumed•…..
Wi-Fi, Bluetooth, Satellite etc.Challenge
• A large variation of the arrival and departmentti f PHEV i t PHEV ki d ktime of PHEVs into a PHEV parking deck
• The number of PHEVs in a parking deck at a time has a large variation with limited amount power supplied from utilities.• Need Low cost and effective communications with sufficient bandwidth to pass information among PHEVs and thecontrollers to effectively perform the charging and discharging• Need effecti e optimal charging/discharging controller algorithms to ork seamlessl ith tilities and PHEV c stomers
Future Renewable Electric Energy Delivery and Management Systems Center
• Need effective optimal charging/discharging controller algorithms to work seamlessly with utilities and PHEV customersunder large uncertainties, and make decisions in real-time with limited bandwidth to communicate among all the entities
Intelligent Energy Management System for PHEVS at a Municipal Parking Deck a u c pa a g ec
Priority Based Allocation Formulation Motivation: Would like all vehicles SoC high to prevent starvation of any vehicle
∑∑Charger
Capacity required, ( )r iC k
( )iI k
' ( )iV k
( )ip kCharger
Capacity required, ( )r iC k
( )iI k
' ( )iV k
( )ip k
where
and p: allocated power for each car at time k,i.e., pi(k) for i∈[1, …, N]
min ( )p
J k
, ( ) (1 ( ))r i i iC k SoC k C= − ⋅
( )( ) ( )i ij i
J k w k SoC k j= − +∑∑ , ( )a iC k( )iSoC k
, ( )a iC k( )iSoC k
wi(k): the priority assigned for to vehicle i at time step kCurrently, we assign priorities based on capacity required and time remaining:where α1 and α2 are weighting coefficients. 1 , 2
1( ) ( )( )i r iw k C k
T kα α= +
Simulating the
1 , 2, ( )i r i
r iT k
Data Acquisition
Simulating the time of plug in
Completed3
Vehicle Information2
Notify Plug in1
TransportDelay
Pulse for triggering plug -in Trigger Acq Acquired Information
plug_in_trigger
Charge
Completion
Plugin
Data _trigger
Begin _charge
Completed
update
DataAcqIDiscrete event decisions
Comparison of Allocation Strategies
100
120
t (%
)
Equal PriorityAssgnmt
State Machine for Station I
UpdateI5 VA
4
Chart
Battery_Charger subsystem I
Begin
Current
P_allc
P
SOC
Completed
VA
Update
CompletedI
SOCI
PI
InputRate and Allocated
Power
1
H b id S t 0
20
40
60
80
SoC
at P
lug-
out Assgnmt
Dynamic PriorityAssgnmtOptimal Allc for SoCMaximization
Future Renewable Electric Energy Delivery and Management Systems Center
Charging with allocated current
Continuous Dynamics
Hybrid System 0Car A Car B Car C Car D Car E
Vehicle
Intelligent Energy Management System for PHEVS at a Municipal Parking Deck
Highlights:
Have prototyped iEMS algorithms on a PHEV Municipal Parking Deckin
a u c pa a g ec
Have prototyped iEMS algorithms on a PHEV Municipal Parking DeckinMatlab/SimulinkHave developed a Graphical User Interface in Labview to conceptualize thesystem operationHave investigated the communication network with ZigBee
Current Work and Expected Milestones:
Further developing iEMS architecture along with implementation in FREEDMand ATEC demonstration testbed in Matlab/Simulink and Labview.Simulating real-world parking deck scenarios with random vehicles arrivals,
initial PHEVs states, time of availability using Monte Carlo method.Integrating the iEMS and demand side management programs into the existingIntegrating the iEMS and demand side management programs into the existingtestbed to alleviate the peak load demand.
Related Publication:1) P Kulshrestha L Wang M Y Chow and S Lukic “Intelligent Energy Management System Simulator for PHEVs at Municipal Parking Deck1) P. Kulshrestha, L. Wang, M.-Y. Chow, and S. Lukic, “Intelligent Energy Management System Simulator for PHEVs at Municipal Parking Deck
in a Smart Grid Environment,” in Proceedings of IEEE Power and Energy Society General Meeting, Calgary, Canada, 2009. (invited)2) P. Kulshrestha, K. Swaminathan, M.-Y. Chow, and S. Lukic, “Evaluation of ZigBee Communication Platform for Controlling the Charging of
PHEVs at a Municipal Parking Deck,” in Proceedings of IEEE Vehicle Power and Propulsion Conference, Dearborn, Michigan, U.S.A, Sept 7-11,2009.
3) W. Su, M.-Y. Chow, “An Intelligent Energy Management System for PHEVs Considering Demand Response,” in Proceedings of 2010 FREEDM
Future Renewable Electric Energy Delivery and Management Systems Center
Annual Conference, Tallahassee, Florida, U.S.A, (Submitted)4) W. Su, M.-Y. Chow, “Evaluate Intelligent Energy Management System for PHEVs Using Monte Carlo Method,” (Draft)
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