study on global optimization and control strategy development … · 2019. 12. 31. ·...

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 61, NO. 6, JULY 2012 2431

Study on Global Optimization and Control StrategyDevelopment for a PHEV Charging Facility

Feng Guo, Student Member, IEEE, Ernesto Inoa, Student Member, IEEE,Woongchul Choi, and Jin Wang, Member, IEEE

Abstract—This paper provides a full study of a photovoltaic(PV)-aided plug-in hybrid electric vehicle (PHEV) charging fa-cility by investigating the two most challenging technical issues:1) sizing of the local energy storage (LES) unit and 2) controlstrategies of the facility. First, the paper proposes a method fordetermining the optimal size of LES for a charging facility. Second,the paper develops a control strategy for the integration of thePHEV charging stations with the proposed LES and PVs. Theproposed LES-sizing method, which is based on optimal controltheory, minimizes a cost function based on the average valueof kilowatt-hour price, irradiance, and PHEVs’ usage patterns.A power-loss/temperature-based battery model and a tempera-ture-based charging strategy previously developed by the authorsare utilized to determine the optimal LES size. Afterward, withthe optimized facility parameters, a detailed circuit model of thecharging facility, including PVs, PHEVs, and LES, is constructedwith a real-time simulation system. While an experimental setupfor this kind of complex and high-cost system was not readily feasi-ble, real-time simulation was carried out to prove the effectivenessof the proposed control strategy. To validate the effectiveness andaccuracy of the real-time simulation, control hardware-in-the-loop(HIL) and power-inverter-based experiments have been carriedout at the subsystem level.

Index Terms—Battery sizing, optimization, plug-in hybrid elec-tric vehicle (PHEV) charging, power electronics interface circuit,real-time simulation, renewable-energy resources.

I. INTRODUCTION

W ITH RECENT concerns about global warming andpetroleum-based energy shortages, the number of plug-

in hybrid electric vehicles (PHEVs) is expected to rapidlyincrease in the coming years [1]. Naturally, it becomes an urgenttask to establish the infrastructure accordingly, along with thepotentially fast growing number of electric vehicles. In doingso, it also becomes critical to understand the interaction amongthe power electronics devices within those infrastructures andpower grid. To study those interactions and to eventually designand control those charging facilities for PHEVs, this paper

Manuscript received June 10, 2011; revised November 10, 2011 andJanuary 28, 2012; accepted March 19, 2012. Date of publication May 3, 2012;date of current version July 10, 2012. This work was supported in part by theU.S. Department of Energy under project number DE-OE-0000402. The reviewof this paper was coordinated by Prof./Dr. L. Guvenc.

F. Guo, E. Inoa, and J. Wang are with The Ohio State University, Columbus43210 USA.

W. Choi is with the Graduate School of Automotive Engineering, KookminUniversity, Seoul 136-702, Korea.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2012.2195787

presents a full study of a virtual charging facility for logisticallyimportant delivery trucks. While a variety of configurations forthe charging facility can be considered particularly to maximizethe use of locally available energy sources, depending on the ge-ographical locations, in the current research, it is assumed thatthe facility takes power from the power grid, as well as from thephotovoltaic (PV) shades. Furthermore, a lithium-ion batterypack is also installed in this facility as a local energy storage(LES) system to lower the overall cost of installation andoperation by buffering the power supplied from the grid and PV,as well as the electric demand from the chargers for the PHEVs.

In the assumed configuration for the charging facility, thetotal size of the PV shades can be readily decided in harmonywith the size of the roof or the shades of the vehicles. However,to determine the size of the LES is quite challenging and canonly be achieved through careful analysis of the PV powergeneration, the cost of grid power, the cost of LES, and thecontrol strategy of the charging facility itself.

Several recent papers [2]–[6] and IEEE standards/guidelines[7], [8] have been published to address many different aspectsof battery sizing for PV installations. Nevertheless, no studyhas been reported to be tightly related to the case of PHEV loadprofiles, which include vehicle-charging strategies and usagepatterns. Most published papers deal with scenarios with PVand lead-acid batteries in use. For instance, Stevens et al. [9]studied the effect of different battery-bank sizes on the perfor-mance of a PV system, whereas Jenkins et al. [10] studied theeffect of size on the lifetime of the battery itself; Chee et al. [11]optimized the battery size based on the probability of survivingan outage while maximizing savings due to demand shift; andSaito et al. [12] optimized with respect to the environmentalimpact of the battery size.

In addition to the sizing of LES, the control strategy of themultiple power electronics circuits in the charging facility alsopresents a great challenge, particularly in the case of intentionalislanding, where the LES and PV panels are solely responsiblefor supplying power to the loads [13]. In [14], Huang et al. de-scribed the different control strategies in grid-connected modeand islanding mode. Several authors in [15] and [16] proposeddifferent control methods to guarantee seamless transition fromgrid-connected mode to islanding mode. However, when itcomes to the case of charging facility, special consideration isstill needed.

In this paper, two major aspects for the optimization of thecharging facility were analyzed. First, a battery-sizing algo-rithm was developed through the minimization of the total

0018-9545/$31.00 © 2012 IEEE

2432 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 61, NO. 6, JULY 2012

energy cost of the charging facility. To model the energy con-sumption, a battery model and optimized charging profiles thatare based on the power loss and temperature change incurredin the battery during the charging process were utilized in thisstudy [17].

Second, with the optimized size of the LES, a detailedcircuit model of the whole charging facility is developed. Inaddition, a comprehensive control strategy was proposed tocoordinate the grid-connected inverters and converters in thesystem during both grid-connected mode and islanding mode.Furthermore, special consideration was given to guarantee theseamless transition of the facility from the grid-connected modeto the islanding mode. While experimental setup for this kindof complex and high-cost system was not readily feasible, real-time simulation was carried out to prove the effectiveness ofthe proposed control strategy. Before the simulation results ofthe whole system were presented, subsystem-level hardware-in-the-loop (HIL) test and experimental test were performed toverify the real-time simulation model and platform.

This paper is divided into five sections. Section II presentsthe battery-sizing algorithm, whereas Sections III and IV showthe proposed control strategy and real-time simulation results.In Section V, conclusions from the current investigation andfinal remarks are presented.

II. BATTERY-SIZING ALGORITHM

A. Algorithm Overview

The proposed battery-sizing algorithm is developed in thecontext of a distribution center in which PHEV distributiontrucks are docked and PV shades are installed. Essentially, theproposed algorithm is based on the minimization of the costfunction defined in (1), in which the monetary cost of the energyconsumed is calculated as

TotalCost = Cbp|day + Cpv|day+

∫day

(PLoad(t)− PPV(t)− PLES(t)) · Ckwh(t) dt

(1)

where the first two terms, i.e., Cbp and Cpv, are the dailyprorated installation/maintenance cost of the LES and the PVsystem, respectively. The proration, which is the conversion ofthe total costs into daily values, is important to have all the costsbased on the same units of time. PLoad(t), PPV(t), and PLES(t)represent the power requirement of the load, the power obtainedfrom the PV system, and the power extracted from the LES as afunction of time, respectively. Finally, Ckwh(t) is the price perkilowatt-hour (kWh), which is set by the utility company. Thus,the term within the integral reflects the daily operation cost ofthe facility.

Intuitively, the first term in (1) tends to increase in a relativelylinear pattern as the size of the LES increases. The last term of(1) tends to decrease in an exponential pattern as the size of theLES increases. Therefore, a minimum for (1) could be foundwhen the size of the LES is varied.

By translating different source/load profiles and subsystemsizes into their resultant monetary cost, the battery-sizing al-

Fig. 1. Battery-cell equivalent circuit.

gorithm can be applied to an unlimited number of source/loadconfigurations.

The algorithm contains three steps.

1) After the profiles of load, irradiation, and kWh price arefound, pick a small size for the LES as a starting point.

2) Apply an optimization tool (GPOPS [18]–[21]) to min-imize (1), which results in a minimized daily cost forthe picked battery size. Basically, GPOPS will mini-mize the integral term in (1) by determining an optimalcharging/discharging profile for the LES, i.e., when theelectricity price is high, the LES will send power to thefacility, whereas when the electricity price is low, the LESwill absorb power from the grid.

3) Linearly change the battery size, and repeat step 2) toform a curve that shows the relationship between dailycost and the sizing of the LES. An optimized LES sizewill be identified as the minimum in this curve.

B. Load Profile

To apply the described algorithm, the profiles of load, irra-diation, and kWh price are needed. Even though the proposedLES-sizing algorithm will work just as good, regardless of theload profile used, a load profile that maximizes the performanceof the Li-ion battery is used [17].

According to the results obtained in [17], in cold weather,the performance of a PHEV’s battery increases if most of thecharge is applied at the end of the charging process, whereas, inhot weather, constant current/constant voltage is near optimalin terms of charging efficiency [22]. The optimal load profileobtained is based on the Li-ion battery model shown in Fig. 1.

Fig. 1 shows an equivalent circuit model for a Li-ion batterycell. This circuit is the same as the standard Randle equivalentcircuit, which is composed of an ideal voltage source, an inter-nal resistance R0, and two parallel RC elements. R0 representsthe dc impedance, whereas the two RC elements represent theshort- and long-time constants for transient responses. Voc,which is a dependent voltage source, represents the amount ofenergy that is available in the battery cell. Its voltage level is afunction of the state of charge (SoC), which is the percentageof charge that is currently available in the battery cell. Theparameters of this model were found using genetic algorithmtechniques on a commercially available PHEV Li-ion batterycell [23]–[26]. These parameters are very nonlinear, and tableswere used to describe their dependence on SoC, temperature,and charging or discharging status. Although this model is not

GUO et al.: GLOBAL OPTIMIZATION AND CONTROL STRATEGY DEVELOPMENT FOR PHEV CHARGING 2433

Fig. 2. Load profiles during summer season and winter season.

Fig. 3. Irradiation profiles for summer and winter days at The Ohio State University.

very sophisticated, generally, it can approximate the batterybehavior very well.

Based on this model, dynamic equations are obtained byconsidering the current as the control input and choosing thestate variables to be

�x = [SoC vc1 vc2 ∆T ]T (2)

where SoC is the state of charge of the battery, ∆T is thetemperature difference between the hottest point in the batteryand the ambient temperature, and vc1 and vc2 are the capacitorvoltages. Therefore, the dynamic equations of the battery, instate-space form, becomes

x1 =−1

3600 · C(x4 + Tamb)u

x2 =−1

R1(x1, x4 + Tamb) · C1(x1, x4 + Tamb)

× �x2 +1

C1(x1, x4 + Tamb)u,

x3 =−1

R2(x1, x4 + Tamb) · C2(x1, x4 + Tamb)

× �x3 +1

C2(x1, x4 + Tamb)u

x4 =−1

Rtemperature · Ctemperature�x4 +

1Ctemperature

Ploss

(3)

where C(x4 + Tamb) is the battery’s capacity, and Tamb is theambient temperature. Rtemperature and Ctemperature accountfor the battery’s thermal model; Ploss is the power loss in theresistive components of the battery model, which is determinedby the charging profile.

As indicated in the previous discussion, optimal chargingprofiles are considered as the load profiles for the LES-sizingoptimization. The values of load profiles are obtained under theassumption that the PHEV’s batteries are charged from 20% to80% (i.e., 3 kWh). These load profiles are shown in Fig. 2.

Since a distribution center setting is assumed, two groupsof delivery trucks (PHEVs) are considered. It is assumed thateach group is composed of five trucks and that each truck hasa battery pack similar to the commercially available 5-kWhHyMotion module. The first group of delivery trucks leavesthe premises at 8:00 A.M., whereas the second leaves at9:00 A.M. After working for four hours, the trucks return to thedistribution center and stay there for an hour. During this time,their batteries are recharged. With the batteries fully charged,the delivery trucks do an evening round of 4 h before returningto the delivery center, where they stay overnight. According to[17], during winter, the PHEVs are charged just before theyleave. Hence, there are two load peaks in the winter load profile:before 9:00 A.M. and before 2:00 P.M. Nevertheless, there isonly one load peak at the middle of the day during the summerseason since, at this time of the year, charging overnight isnear optimal. The peak happens when the PHEVs return to thedelivery center to be recharged before leaving for the eveningshift.

C. PV Irradiation Information

Fig. 3 shows the irradiation profiles for one summer dayand one winter day. To test the LES-sizing algorithm, it isassumed that ten PV strings are utilized. These PV strings havean efficiency of 15% and an area of 12 m2 each. The installedPV capacity in the algorithm is 18 kW-peak. Assuming anestimated cost of $US 8000 for kW-peak and a life span of


Fig. 4. Cost of commercial kWh versus time for winter and summer days.

Fig. 5. Daily cost during winter without PV.

20 years, the prorated equivalent cost for the PV system be-comes $US 19.73/day.

D. Electricity Price

The price of kWh by utility company is the last informationneeded in (1) to implement the LES-sizing algorithm. Thisinformation is publicly available and has been plotted in Fig. 4for two representative days.

E. Calculation Results

Finally, it is assumed that the charger of the LES is LevelII, and the cost estimated for Li-ion batteries provided bythe U.S. Department of Energy [27], of $US 300/kWh and$US 500/kWh, were both used to show the influence of thisparameter to the calculation. These cost estimates assume bat-teries with a life span of 15 years (15 Y), as expressed in thelegends of Figs. 5–7.

Results for different cases are plotted in Figs. 5–7. Figs. 5and 6 show the results for the load profiles used in cold weather,without and with a PV system installed. The minima are at thesame values as the LES size since the amount of received solarenergy is not high enough to make a major impact of the dailyoperation cost.

The comparison between Figs. 5 and 6 shows that the in-stallation cost of the PV systems has major impact on theoverall daily cost of the system, which includes the proratedinstallation/maintenance cost and operation cost.

Fig. 7 shows the result for a similar system in hot weather,where a different load profile is used. Since it is during the

Fig. 6. Daily cost during winter with PV.

Fig. 7. Daily cost during summer with PV.

middle of the boreal summer season, there is an increase inthe intensity and length of irradiation obtained. Therefore, itmakes economic sense to have a bigger LES to save the extrasolar energy, so that it can be used when the load demands it.For example, if the cost of the battery is $US 300/kWh, theminimum happens at the LES size of 11 kWh, which is 1 kWhlarger than the case presented in Fig. 6.

It can be seen from Figs. 6 and 7 that the optimized batterysizes for the hottest and coldest days are very close to eachother. This means that the atmospheric condition does not havemajor impact on the battery sizing, even though it indeed hashigh impact on the daily cost. In addition, from Figs. 5–7, itcan be seen that the parameter of the highest influence is thekWh cost of the battery. These results emphasize the importantinsight that can be gained by using the proposed algorithm.

III. CONTROL STRATEGY FOR THE CHARGING FACILITY

A. System Description

To integrate the LES and PVs into a PHEV charging stationand realize the operation criteria discussed in Section II, asystem-level PHEV charging station model is built with anadvanced real-time simulation system. The schematic of thefacility and detailed circuit topology for each branch are shownin Fig. 8.

The charging station is able to work in two modes: grid-connected mode and islanding mode. During grid-connectedmode, the circuit breaker is closed. The operation of the LESfollows the rule that, when the electricity price is high, theLES will send power to the charging facility; when the priceof electricity is low, the LES will absorb power from the grid.


Fig. 8. System schematic and the detailed circuit topology. (a) System schematic. (b) Circuit topology for each branch.

Once a failure happens on the grid side, the circuit breaker willopen, and the charging facility will operate in islanding mode.Under this condition, the LES and PVs will provide power tocharge PHEV batteries. No matter which mode they are, thePVs will always be controlled to output maximum power to thecharging facility.

According to Fig. 8(b), the equation to describe the voltageand current relationship on the ac side can be expressed as

vabc = Rf iabc + Lfd

dtiabc + vabc1 (4)

where Rf and Lf are the line resistance and inductance, respec-tively. Using the transformation matrix in (5), shown at the bot-tom of the page, where “f” represents any electrical parameter,(4) can be transformed into a d− q rotating reference frame.After transformation, equations are given in (6), shown at thebottom of the page.

Meanwhile, in the d− q rotating reference frame, the activeand reactive power can be expressed as

P = vdid + vqiq

Q = vdiq − vqid. (7)

To simplify the analysis, the rotating d-axis is synchronizedwith the grid voltage vector, which makes vq = 0. Neglectingharmonics and losses in the circuit, the relationship between theac side and the dc side can be found as

vdcidc = vdid. (8)

B. Control Strategy

The aim of the controller design is to make sure that the sys-tem properly works in both grid-connected mode and islandingmode. Meanwhile, the LES, PVs, and PHEV batteries should becontrolled to operate under the criteria discussed in Section II.More importantly, the controller should avoid large voltage andcurrent spikes during the system transition from grid-connectedmode to islanding mode.

1) Grid-Connected Mode: For the grid-connected mode, amultiloop controller is used for all the three grid tied inverters,as shown in Fig. 9(a). The control objective of the invertersis to keep the dc-link voltage constant. Therefore, the outerloop of the controller is a voltage loop with dc-link voltage asthe feedback. The inner loop is a grid current loop, with the

fdfqf0

=

√23

cos(ωt) cos(ωt− 2π/3) cos(ωt+ 2π/3)− sin(ωt) − sin(ωt− 2π/3) − sin(ωt+ 2π/3)

1√2

1√2

1√2

fafbfc

(5)

vd =Rf id + Lfdiddt

− ωLf iq + vd1

vq =Rf iq + Lfdiqdt

+ ωLf id + vq1 (6)


Fig. 9. Control strategy of the LES-connected inverter in grid-connected mode. (a) Control structure of the dc/ac inverter. (b) Simplified control diagram.

Fig. 10. Bode plots of the current and voltage closed-loop transfer functions in grid-connected mode. (a) Current control loop. (b) Voltage control loop.

active current reference generated by the outer voltage loop andthe reactive current reference set to 0. Based on (6)–(8), thesimplified dc bus output-voltage-to-reference-voltage controldiagram is shown in Fig. 9(b).

To design a system with a fast dynamic response and smallsteady-state error for the dc-link voltage, the inner current loopshould have a much faster response than the outer voltage loop[28]. Therefore, on one hand, the bandwidth of the currentloop should be designed as high as possible. On the other hand,the bandwidth should be designed to remove the switchingharmonics; therefore, it should be no more than half of theswitching frequency [29]. Referring to Fig. 9(b), the closed-loop current transfer function is shown in

IdI∗d

=Kcps+Kci

Lfs2 + (Kcp +Rf )s+Kci(9)

where Kcp and Kci are the proportional and integral gain of thecurrent loop proportional–integral (PI) controller, respectively.

During the voltage loop controller design, it is assumed thatthe current loop is ideal. Again, a PI controller is applied to getthe required system response characteristics. The closed-looptransfer function for the voltage loop is shown in

Vdc

V ∗dc

=Kvps+Kvi

Cs2 +Kvps+Kvi(10)

where C is the dc bus capacitance, and Kvp and Kvi are theproportional and integral gain of the voltage loop PI controller,respectively.

The resulting bode plots of the system current and voltagecontrol loop are shown in Fig. 10(a) and (b), respectively. Ascan be noticed, both current loop and voltage loop have enoughphase margins to guarantee the stability of the system. Thebandwidth of the current loop is bigger than the voltage loopand has a faster dynamic response, which is consistent with thedesign requirement.


Fig. 11. Control strategy of the LES-connected inverter in islanding mode.(a) Control structure of the dc/ac inverter. (b) Simplified control diagram.

Considering three converters, the LES-connected converterand PHEV-battery-connected converter are working in currentcontrol mode. Their current commands are determined bythe criteria described in Section II. On the other hand, thePV-connected converter is working with maximum power pointtracking strategy.

2) Islanding Mode: For the islanding mode, the invertersand converters that are connected to the PVs, and the PHEV bat-teries still keep the same control strategy as in grid-connectedmode. At the same time, the LES is controlled as a swingbus to generate constant ac voltage. For the LES inverter,the same multiloop control structure is used, but with theac bus voltage as the feedback in the outer voltage loop, asshown in Fig. 11(a). There is no change in the inner currentcontroller [30]. Because of the minimized change in the controlstrategy from the grid-connected mode to the islanding mode, aseamless transition from the grid-connected mode to islandingmode can be achieved by this design. The simplified ac busoutput-voltage-to-reference-voltage control diagram is shownin Fig. 11(b).

The design process of the multiloop controller is the sameas that in grid-connected mode. Because the ac bus is notconnected to the grid, the influence of the ac side capacitorneeds to be considered during the controller design, as shownin Fig. 11(b). However, the inner current loop can still use the

same PI controller. The open- and closed-loop transfer functionare shown in

IdV ∗dl

=Cfs

LfCfs2 +RfCfs+ 1(11)

IdI∗d

=KcpCfs+KciCf

LfCfs2 + (KcpCf +RfCf )s+ (KciCf + 1)(12)

respectively, where Cf is the ac line capacitance.The outer voltage loop should have a slower response than

the current loop. The open- and closed-loop transfer functionsfrom the reference ac voltage to the output ac voltage are givenby (13) and (14), shown at the bottom of the page, where K ′

vp

and K ′vi are the proportional and integral gain of the voltage

loop PI controller, respectively.The resulting bode plots of the system current and voltage

control loops are shown in Fig. 12(a) and (b). As shown, bothcurrent loop and voltage loop have enough phase margins, sothe system is stable. At the same time, the current controlloop has a relatively large bandwidth, which means that thedynamic response of the control loop is fast and can track thereference well.

As previously mentioned, in islanding mode, the LES-connected inverter is used to control the ac bus voltage. There-fore, the dc/dc converter is controlled to regulate the dc-linkvoltage. Considering that the output current of the LES shouldalso be tightly controlled, a multiloop controller with outervoltage loop and inner current loop is designed for this con-verter. The outer voltage control loop uses dc-link voltage asthe feedback, which will generate a reference value for the innercurrent control loop. The control structure is shown in Fig. 13.

IV. SIMULATION AND EXPERIMENT RESULTS

The proposed charging facility topology contains differentkinds of renewable-energy resources and storage devices, suchas PV panels and battery storage systems, and also a large num-ber of power electronics interface circuits. This makes it quiteexpensive and difficult to build a test bed with real equipment.Under these circumstances, the real-time simulation providesa comprehensive and effective way to study the facility. Thereal-time simulation platform used in the laboratory is based onthe personal computer technology running a Linux operatingsystem. It consists of four real-time simulators with a totalof eight CPUs, 48 cores, five field-programmable gate arraychips, and more than 500 analog and digital inputs/outputs [31].Dolphin [32] peripheral component interconnect boards areused to provide an extremely high speed and low-latency real-time communication link between simulators. The platformhas a powerful computation capability to precisely simulate acomplex power system model at the wall clock. In addition,

Vd

I∗d=

Kcps+Kci

LfCfs3 + (RfCf +KcpCf )s2 + (KciCf + 1)s(13)

Vd

V ∗d

=KcpK

′vps

2 + (KcpK′vi +KciK

′vp)s+K ′

viKci

LfCfs4 + (KcpCf +RfCf )s3 + (1 +KcpK ′vp +KciCf )s2 + (KcpK ′

vi +KciK ′vp)s+K ′

viKci(14)


Fig. 12. Bode plots of the current and voltage closed-loop transfer functions in islanding mode. (a) Current control loop. (b) Voltage control loop.

Fig. 13. Control structure of the LES-connected converter in islanding mode.

Fig. 14. HIL test. (a) System schematic. (b) HIL setup.

with the support of the real-time simulator, HIL can be utilizedto test prototype controllers before implementing them into anactual system.

A. Single-Inverter Test

A grid-connected inverter is implemented with both a HILsetup and an experimental setup. The inverter is controlled toinject power to the grid with unity power factor. During thesetwo tests, the same control board and control algorithm areused. The parameters in these two tests are almost identical,except for parasitic parameters. The HIL test setup is shownin Fig. 14. The inverter, the grid, and loads are simulated atreal time within the real-time simulator. The controller is aTMS320F2812-based digital-signal processing (DSP) board. Inthe simulation model, voltages and currents are measured. Themeasurement results are converted to analog signal to simulatesensor outputs in a real system. The DSP board takes in theseanalog signals as feedback and digitizes them with onboardanalog-to-digital converters. Then, based on these feedbacks,the DSP generates required pulsewidth modulation gating sig-nals and feeds them to the real-time simulator.

Fig. 15. Single-inverter experimental test setup.

The experimental scenario is the same as the HIL-based test.A three-phase inverter is controlled to inject current to the grid,as shown in Fig. 15. The grid-side voltage is set at 50 V, andthe switching frequency is 10 kHz. The same control board andcontrol algorithm are used in both tests.

The test results from the HIL setup and the experimentalsetup are shown in Fig. 16(a) and (b), respectively. The results


Fig. 16. Results for the single-inverter test. (a) HIL result. (b) Experimental result.

TABLE ISIMULATION PARAMETERS

from the HIL system were measured at the analog outputport of the real-time simulator, whereas the results from theexperimental setup were directly measured from the inverter.The grid-side line-to-line voltage, phase current, and inverterdc bus voltage are shown in both Fig. 16(a) and (b). In bothcases, the phase current is lagging the line voltage by 30◦, andthe unity power flow is achieved with the control strategy.

It is shown that the HIL test results and experimental testresults are almost identical with each other, so it proves thatthe control strategy for a single inverter is effective, the simu-lation model is correct, and the real-time simulation results aretrustable.

Even though the charging facility is much more complexthan the single inverter, the real-time simulation of the facilityonly requires 50% capability of one of the eight CPUs of thesimulator. Thus, the system-level simulation is highly reliable.

B. System-Level Test: Grid-Connected Mode

The whole system and the control strategy aforementionedare then modeled in the real-time simulation system, with theparameters shown in Table I.

To verify the dynamic response of the control strategy duringgrid-connected mode, it is assumed that, at a given time, theelectricity price is low, and the LES absorbs 2-kW power fromthe grid; later on, the electricity price goes up. Thus, the LESbegins to send 7-kW power out to the facility.

Again, with the analog input/outputs of the real-time simu-lator, simulation results could be observed via an oscilloscope.Oscillograms of the LES output dc current and voltage, ac busvoltage, and LES output ac current are shown in Fig. 17(a),

whereas the grid-side ac current and PHEV charging current areshown in Fig. 17(b). As a reference, the LES output dc currentis shown in both Fig. 17(a) and (b). The results show that the dcbus voltage, ac bus voltage, and PHEV charging current remainconstant when the output power of the LES changes; when theoutput power of LES is increased, the facility gets less powerfrom the grid, and the grid-side current decreases. The systemreaches steady state in one fundamental cycle, which meansgood dynamic performance of the proposed control strategy.

C. System-Level Test: Intentional Islanding

To further verify the control strategy, real-time simulation iscarried out for the case where the system transfers from grid-connected mode to islanding mode. It is assumed that, at acertain time, a three-phase short circuit happens on the gridside. The protection unit detects this failure and opens the maincircuit breaker. To simplify the simulation, it is assumed that,in the grid-connected mode, right before the circuit breakeropens, the LES is sending 7-kW power to the system, and in theislanding mode, the LES and PVs can provide enough power tocharge PHEV batteries.

The real-time simulation results of the internal dc bus voltageof the LES, the ac bus voltage, ac currents from the LES-connected inverter, currents from the power grid, and PHEVcharging currents are shown in Fig. 18. The result illustrates thatthe system correctly operates in islanding mode. Furthermore,the voltage and current spike during the transition from grid-connected mode to islanding mode are within four times oftheir normal values, which are not significant. Therefore, theproposed control strategy is proven to be effective.

V. CONCLUSION

This paper has dealt with two important issues in scenarioswith high level of penetration of PHEVs and renewable-energysources: 1) sizing of the LES devices and 2) controlling ofmultiple inverter/converter systems in the charging facility.

The battery-sizing algorithm minimizes a cost function basedon the value of kWh price, irradiance, and PHEVs usagepatterns. A power-loss/temperature model and a temperature-based charging strategy have been embedded in the costfunction. Detailed design process and optimization results in


Fig. 17. Simulation results in grid-connected mode. (a) LES output dc current and voltage, ac bus voltage, and LES output ac current. (b) LES output dc current,grid side ac current, and PHEV charging current.

Fig. 18. Simulation results in islanding mode. (a) Circuit breaker signal, LES dc bus voltage, ac bus voltage, and LES output ac current. (b) Circuit breakersignal, LES dc bus voltage, grid side ac current, and PHEV charging current.

both winter and summer days have been presented in thepaper. Although it is developed under a specific condition, thisalgorithm can be utilized as a generic approach to determine thesize of the LES in charging facilities.

Once the method of deriving major design parameters ofthe charging facilities is achieved, the remaining challenge ishow to control the multiple energy resources and LES devicesin the charging facilities. Thus, detailed control strategies fora hypothetical charging facility have been proposed. Boththe grid-connected mode and the islanding mode have beenstudied. The proposed control strategies require minimalchange in the control loops when the system transfers betweenthe grid-connected mode and the islanding mode. The wholesystem, including the control strategies, has been modeled inan advanced real-time simulation platform. Simulation resultshave shown that the proposed control strategies have verygood dynamic performance. The subsystem-level HIL andexperimental test results have shown the high fidelity of thereal-time simulation platform.

In summary, the sizing of LES devices and system controlstrategies are the two major technical challenges in the designand operation of renewable-energy-assisted charging facilities.The comprehensive study that has been presented in this paperprovides general guidance and pathways to solve these chal-lenging problems.

ACKNOWLEDGMENT

This work was supported in part by the Korean ElectricVehicle-Transportation Convergence System Research Consor-tium (KEVTRAC) from the Ministry of Land, Transportation,and Maritime affairs of Korea and Kookmin University. Theauthors would also like to thank Prof. L. Xu and his group fortheir help with the experimental setup.

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Feng Guo (S’09) was born in Henan, China, in 1986.He received the B.S. degree in electrical engineeringfrom Wuhan University, Wuhan, China, in 2009. Heis currently working toward the Ph.D. degree fromThe Ohio State University, Columbus.

His current research interests include large-scalephotovoltaic power plant, hardware-in-the-loop andreal-time simulation of smart grid, power electronicscircuits in hybrid electric vehicles, and energy har-vesting around high-voltage transmission lines.

Ernesto Inoa (S’10) was born in The DominicanRepublic. He received the B.S. degree in electron-ics engineering (magna cum laude) from PontificiaUniversidad Católica Madre y Maestra, DominicanRepublic, in 2000 and the M.S. degree in electricaland computer engineering from the University ofCentral Florida, Orlando, in 2005. He is currentlyworking toward the Ph.D. degree from The OhioState University, Columbus.

He was an Automation Engineer for CND, a Do-minican brewing company, where he helped auto-

mate several bottling lines with the use of programmable logic controllers.His research interests are the application of novel control techniques to powerelectronics, motor drives, and energy conversion systems.

Woongchul Choi was born in Seoul, Korea, in 1964.He received the B.S. degree in mechanical engineer-ing from Seoul National University in 1987 and theM.S. and Ph.D. degrees in mechanical engineeringfrom The Ohio State University, Columbus, in 1989and 1995, respectively.

He is currently a Professor with the GraduateSchool of Automotive Engineering, Kookmin Uni-versity, Seoul. His research interests are energyanalysis and modeling for various types of electricvehicles including hybrid, fuel cell hybrid and pure

electric vehicles, and system integration.

Jin Wang (S’02–M’05) received the B.S. degreefrom Xi’an Jiaotong University, Xi’an, China, in1998, the M.S. degree from Wuhan University,Wuhan, China, in 2001, and the Ph.D. degree fromMichigan State University, East Lansing, in 2005,all in electrical engineering.

From September 2005 to August 2007, he waswith Ford Motor Company, where he was a CorePower Electronics Engineer and contributed to thetraction drive design of the Ford Fusion Hybrid.Since September 2007, he has been an Assistant

Professor with the Department of Electrical and Computer Engineering, TheOhio State University, Columbus. His teaching position is cosponsored byAmerican Electric Power, Duke/Synergy, and FirstEnergy. His research in-terests include high-voltage and high-power converter/inverters, integration ofrenewable-energy sources, and electrification of transportation.

Dr. Wang has more than 50 peer-reviewed journal and conference proceedingpublications. He has been an Associate Editor for the IEEE TRANSACTIONS

ON INDUSTRY APPLICATION since March 2008. He received multiple teachingand research awards, including the Ohio State University College of Engineer-ing Boyer Award for Excellence in Undergraduate Teaching Innovation in 2012,the National Science Foundation Career Award in 2011, and the IEEE PowerElectronics Society Richard M. Bass Outstanding Young Engineer Award in2011.

study on global optimization and control strategy development … · 2019. 12. 31. ·...

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