Analysis On Optimum Layout Of Charging Station

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Analysis on Optimum Layout ofCharging StationCentral Research Institute of Electric Power IndustryResearch ScientistRyoji HiwatariEV & PHV Town Symposium in TokyoJune 5, 2013,Tokyo Big Sight (Koto Ward, Tokyo)1

Abstract Analysis result on optimization of the layout ofcharging stations (ST) by estimating chargingdemand of electric vehicle (EV) based on thetraffic simulation As the layout method for STs, the followingempirical methods are proposed based on theoptimum layout analysis, condition for preferentialplacement to large city, space of ST placementalong major roads, allocation of ST for majorcities, No. of ST for each prefecture. Basic concept for optimizing the ST layout andoutline of traffic simulator for analyzing charginginfrastructure of next generation vehicles EV@OLYENTOR2

Content1. Introduction : study on EV and charginginfrastructure in CRIEPI2. Outline of H24 sponsored research on OptimumLayout of Charging Station3. Outline of traffic simulator for analyzing charginginfrastructure of next generation vehicles「EVOLYENTOR@」4. What is the optimum layout of charging STs ?5. Analysis result on optimum layout of charging ST6. Layout method for charging STs based on theanalysis of optimum layout7. Summary3

Study on EV and charging infrastructure in CRIEPI Study on the effect on the electric network bythe electric demand, potential of CO2 reduction,diversification of vehicle fuel by introduction ofnext generation vehicle Study on the leveling method of electric demandand the stabilizing method of renewable energyby charging battery of EV (collaboration withUniv. of Tokyo) Development of traffic simulator (EV@OLYENTOR ) for analyzing charging demandand charging infrastructure applicable to thestudy on the effect of EV introduction on theelectric network4

Outline of the sponsored study The optimum layout of charging STs areanalyzed for the development of charginginfrastructure network , and the guideline oflocating a ST to minimize the empty energy risk isproposed. Study items① Analysis of the effect of the present STnetwork for the model regions② Analysis of optimum layout of ST for themodel regions③ Analysis of optimum location for theadditional STs for the model regions④ Proposal of the guideline of locating STs5

Traffic simulator for analyzing charging ST of nextgeneration vehicle:「EV-OLYENTOR@」Pre-post toolMap Database(Digital Map25000,etc)Traffic Data(Traffic census etc.)Census Data( employee, businessfacility etc.)Set up input filesfor trafficsimulator Place charging STs Modify road data Input traffic jam data Specify simulationregionMap and ST location ofKanagawa pref. in JAPANInput files(Excel CSV files) Road map data Charging ST data Trip data Traffic jam data Census data(employee, businessfacility)This system is developed@under “artisoc ”(Kozokeikaku Eng. Inc.)Display resultsTraffic simulatorPre-post toolMap panelOutput panelInput panelConsole panelEV trafficsimulation and SToptimization No. of EV running outof electricity No. of charging EV,etc.6

Demo. of traffic simulation :dead EV :point where warning sign is on of dead EV :EV with warning sign on :commuter use :go home use :random use :out-of-service EV:charging ST7

Model of road network Digital Road Map(Japan Digital Road Map Association) Digital Map 25000 (Geospatial Information Authority of Japan)、Open Street Map( are also available.Map databaseAnalysis target:highway, national rd., major local rd.Map modelRd. width more than 13mRd. width of 5.5m 13m8

Rd. gradient based on elevation dataFig.Elevation of Tottori and Okayama prefs.Fig.Rd. gradient of Tottori and Okayama prefs.9

Effect of rd. gradient on mileage Based on the CRUISE@ system of AVL company inAustria, a fuel mileage simulation model of iMiEV(2010 model, type:ZAA-HA3W) is applied todevelop the mileage function(shown as the 000.40000.30000.20000.1000Fig.Simulation model of i-MiEV on CRUISE.-9-8-7-6-5-4-2-11245798道路勾配 e function applied in traffic simulation10

Algorithm of charging behavior andEV conditionsSet origin and destinationEV conditionsSet route between ODBattery capacityStart from origin or STUnder thresholdfor warning signNoInitial state of charge 𝑆𝑆𝑆𝑖𝑠𝑠𝑠𝑠𝑠ST is nearer thandestinationyesSet route to ST and go to ST, andrecharge at the ST161.0Fuel mileage 𝐿𝑓𝑓 (km/kWh)7.5Threshold for warning sign 𝐸𝑎𝑎𝑎𝑎𝑎(kWh)8.0Accessary poweryesNo𝐶𝑖 (kWh)valuevelocity𝑃𝑎𝑎 (kW)normal rd./hwy. (km/h)Traffic jam effect3.045/80onSet route from ST to destinationArrive at the destinationEV starts the charging behavior tonearest ST just after warning sign on11

Threshold of battery energy forwarning sign SOC 50% starts the chargingbehavior (Fig2-7、Guideline ofinstalling charging infrastructure toparking area, 2012, Ministry of Land,Infrastructure, Transport and Tourism) SOC 50% 60% at the start ofcharging is maximum.( SOC 50% is applied as thetrigger to the charging behavior. However, it is said that learningEV operation enables to makesmall the trigger of SOC forcharging behavior.Fig: condition of charging behaviorin Japan12

EV operation conditions Use:owner-driven car Origin:proportional to employed population Trip length:based on the traffic survey Destination:proportional to No. of company Route:to minimize the trip period from origin todestination Daily trip freq.:based on the traffic surveyProbability of trip frequency1. of trip lengthRef.:Disclosed data from JARI to METI obtained in the standardizationproject of basic technology for integrated battery system (New EnergyPromotion Council)2345Trip frequency per travel67(trips)8Fig.Distribution of trip frequencyRef. The investigation committee for transportation systemfor Imabari city13

Validation of traffic simulation 1 Simulation results(simulation) is compared withperson trip survey(survey) for validationKawasaki2928272526Yokohama24232214

Validation of traffic simulation 2 Simulation results(simulation) is compared withperson trip survey(survey) for validation as fororigin and destination Kawasaki2928272526Yokohama24232215

Algorithm for relocation of STCharging STmoves to chargingdemand areaLayer 2Charging ST LayerUpdate the layout ofcharging STsLayer1EV LayerAnalysis layer where chargingSTs determine the layoutautonomously according tocharging demand Mapping the charging demand(location of dead EVs andwarning sign on )Analysis layer whereEV traffic simulation iscarried out with STs16

Demo. of relocation of ST17

Algorithm for optimizing layout of STs①②③Analyze the locate of warning sign on and the totalelapsed time of warning sign by traffic simulationAccording the ranking of the elapsed time of warning signfor each city, charging ST is tentatively placedCharging STs relocate autonomously according to thecharging demand of warning sign on :point where warning sign of dead EV becomes onKawasaki:3rd Charging demandKanagawa pref.Odawara:2nd Charging demandYokohama:1st Charging demand①EV traffic simulation②ST is tentatively placedaccording to the ranking③STs relocateautonomously accordingto the charging demand18

What is the optimum layout ofcharging STs ? How many STs are required ? The point is two viewpoints from EVuser and from infrastructure company(feasibility of ST operation as business) EV user:The more charging ST number,the more useful for EV user ST company: The more charging STnumber, the less the ST availability(excessive competition) Where should the ST be placed ? Placement priority of STs depends onthe charging demand19

Risk of empty electric energy Risk of empty fuel for the owner-driven car No. of gas station in Japan:about 40000 (2010) Risk of empty fuel: 5.0 10-4% Annual rescue No. for empty /index.htm) No. of the owner-driven car:about 68,000,000 Availability of the owner-driven car(2008):70%(Ministry of Land,Infrastructure, Transport and Tourism) Risk of empty energy for EV No. of charging ST in Japan:1677 (April. 2013) Risk more than 10% is expected under the operation conditionsimilar to the present owner-driven car exclusive of severalprefectures In this report, the target risk of empty electric energy for EVis considered as less than 1%, and the required No. of ST isanalyzed.20

Optimum Layout of STs in this report ST No. required to reduce the dead EV rate less than 1%under the present owner-driven usageNotice : 1% of dead EV ration is just a milestone, and it should bereduced more by improving EV performance and ST placement Placement to area of large charge demand based on trafficsimulation without the present STNotice Several layouts exist corresponding the dead EV rate of 1% Highway, National Rd., major local Rd. is considered Simple route selection for minimum time from origin anddestination Prediction of charge demand is not precise enough to specify thelocation of ST Guideline of ST placement (the required No. of ST for eachcity and prefecture, max. space between ST along the majorroad) is proposed21

Analyzed regions for ST layout Points to be considered for selection of the region Shape of prefecture(complexity of rd. network)Layout of major citiesGeographical characteristics(highland area etc.)Wide simulation area over several prefs.(urban and ruralarea) Selected regions for analysis(6region with10pref.) Tottori and Okayama prefs.Osaka pref.Gifu and Aichi prefs.Tokyo, Kanagawa and Shizuoka prefs.Tochigi pref.Aomori pref.22

Demo. of Tokyo-Kanagawa-Shizuokaprefs. case23

Tokyo-Kanagawa-Shizuoka prefs. case①Metro areaTokyo・Kawasaki・Yokohama②Major roadTomei Highway③Major cityHamamtsu・Shizuoka④Peninsula areaIzu peninsula24

Tottori and Okayama prefs. case④Major cityYonago・Kurayoshi・Tottori③Highland areaDaisen④ Major roadNational Rd. 53・482②Major cityTuyama・Mimasaka② Major roadHwy. of Okayama・chugoku・Yonago① Metro areaOkayama・Kurashiki25

Osaka pref. case② Highland areaNorthern Minou area① Metro areaOsaka② Highland areaKawachinagano26

Gifu and Aichi prefs. case②Major cityTakayama③ Major roadTokaihokuriku Hwy.① Metro areaGifu, Nagoya, Toyohashi⑤ Highland areaNorikura・Okuhida③ Major roadNational Rd. 41② Major roadChuo Hwy.④ Highland areaEastern Aichi area④Peninsula areaChita, Atsumi27

Tochigi pref. case③ Highland areaKanuma・Nikko・Nasu④Major cityNasukarasuyama・Otawara① Major roadTohoku Hwy.Nikko-Utsunomiya Rd.②Major cityUtsunomiya②Major cityAshikaga・Oyama28

Aomori pref. case①Major cityMutsu④Peninsula areaTsugaru①Major cityAomori③ Major roadNational Rd. 279①Major cityHirosaki・Goshogawara② Major roadMichinoku Rd.National Rd. 4・279④ Highland areaShirakami③ Highland areaHakkoda①Major cityHachinohe・Towada29

Procedure of ST placementPattern of ST placement based on theanalysis results①Preferential placement in grid-like fashion tothe Metro area②Placement to the major city③Placement to the major road between majorcities④Placement to peninsula and highland area⑤Placement uniformly to the whole area30

Preferential ST placement to metro area Preferential ST placement if there is a metro areain the target cities for charging ST installation Evaluation index is investigated according to thestate of a prefecture(population, company no., areaetc.) Evaluation index (Population density[person/ 2]) (company density [/ 2]) (area[ 2]) If a city has a standard deviation score more than150, or there are neighbor cities which have itsvalue more than 100, those cities arecorresponding to the preferential placement area31

�圏評価指数偏差値Standard deviation score for all citiesPreferential placementarea:Tokyo 23 wards180160140120100806040200Fig.Standard deviation score of evaluation index for Tokyo-Kanagawa-Shizuoka prefs.32

Standard deviation score for all citiesPreferential placementarea:Nagoya ��西市清須市0Fig. Standard deviation score of evaluation index for Gifu-Aichi prefs.Preferential placementarea:Okayama & Kurashiki180大都市評価指数偏差値160Not preferential ��取市0Fig. Standard deviation score of evaluationindex for Tottori-Okayama prefs.Fig. Standard deviation score of evaluationindex for Tochigi pref.33

Placement of ST along major road Analyze separately highway and general road Highway:ST placement to the servicearea(SA)・parking area(PA) General road:evaluate ST space along themajor road required for fuel empty risk less than1% Averaged ST spaces along 24 national roads areevaluated according to the optimum ST layoutanalysis for fuel empty risk less than 1% (ST space) -0.00064 (24hr. traffic volume) 38.834

Upper limit of ST space(ST space [km]) -0.00064 (24hr. traffic volume [car]) 38.8AveragedST space (km)(km)充電ST設置間隔353025Grid-like placement of5-10km in a metroarea and large cities201510500200004000024時間交通量(台) (car)24hr.Traffic volume60000Highland area(Okutama, Fuji footarea) and peninsula (Izu) without Hwy.35

Placement of ST in the major city No. of ST in a city required for fuel empty risk less than 1% isevaluated as for about 400 cities in the analyzedprefectures. The more frequently the city is selected as origin anddestination, The larger the charging demand. Frequency selected as origin is assumed to be proportional tothe population density Frequency selected as destination is assumed to be proportionalto the company density ST index is defined as a function of area, population, andcompany number based on the correlation analysis ST index (area [ 2])0.68 (population [person])0.2 (No. of company)0.1936

No. of ST for major cities ST index (area [ 2] )0.68 (population [人])0.2 (No. of company)0.19(No. of ST a city ) 0.0006x(ST index) 0.8228充電ST設置数 数The city which recentlymerged with depopulated10000 area has a tendency to be thelower limit.Nakatsukawa37

Summary Analysis on optimization of the layout of charging stations(ST) was carried out by estimating the charging demand ofEV based on the traffic simulation As the layout method for STs, the following empiricalmethods are proposed based on the optimum layoutanalysis, condition for preferential placement to large city,space of ST placement along major roads, allocation of STfor major cities, No. of ST for each prefecture. Increase of sample number of the prefecture is required toimprove the accuracy and to develop an empiricalevaluation method for ST number in each prefecture. The result reported here is based on the tentative target offuel empty risk less than 1%, and the ST No. should andwill be more required than evaluated in this report in orderto improve the EV convenience similar to the present-4owner-driven car(fuel empty risk about 10 % ).38

AcknowledgementWe had kind instruction from and cooperation withthe automobile division of Ministry of economy,trade and industry, Next-generation vehiclepromotion center, Mitsubishi research institute,Japan automobile res. inst., Kozo Keikaku Inst. Inc.on carrying out this sponsored study.We would like to express our appreciation toeveryone concerned with this study.39

Thank you for your attention.If you have any questions orcomments, please contact Hiwatari(

Charging ST Layer Layer1 EV Layer Analysis layer where charging STs determine the layout autonomously according to charging demand Analysis layer where EV traffic simulation is carried out with STs Update the layout of charging STs Mapping the charging demand (location of dead EVs and warning sign on ) Charging ST moves to charging

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