Analysis on Optimum Layout of
Charging Station
Central Research Institute of Electric Power Industry
Research Scientist

Ryoji Hiwatari

EV & PHV Town Symposium in Tokyo
June 5, 2013,

Tokyo Big Sight (Koto Ward, Tokyo)

1Abstract Analysis result on optimization of the layout ofcharging stations (ST) by estimating charging
demand of electric vehicle (EV) based on the
traffic simulation
 As the layout method for STs, the following
empirical methods are proposed based on the
optimum layout analysis, condition for preferential
placement to large city, space of ST placement
along major roads, allocation of ST for major
cities, No. of ST for each prefecture.
 Basic concept for optimizing the ST layout andoutline of traffic simulator for analyzing charging
infrastructure of next generation vehicles EV@
OLYENTOR
2

Content
1. Introduction : study on EV and charging
infrastructure in CRIEPI
2. Outline of H24 sponsored research on Optimum
Layout of Charging Station
3. Outline of traffic simulator for analyzing charging
infrastructure of next generation vehicles「EVOLYENTOR@」
4. What is the optimum layout of charging STs ?
5. Analysis result on optimum layout of charging ST
6. Layout method for charging STs based on theanalysis of optimum layout
7. Summary3

Study on EV and charging infrastructure in CRIEPI Study on the effect on the electric network by
the electric demand, potential of CO2 reduction,
diversification of vehicle fuel by introduction ofnext generation vehicle
 Study on the leveling method of electric demand
and the stabilizing method of renewable energy
by charging battery of EV (collaboration with
Univ. 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 charging
infrastructure network , and the guideline oflocating a ST to minimize the empty energy risk is
proposed.
 Study items① Analysis of the effect of the present ST
network for the model regions② Analysis of optimum layout of ST for themodel regions③ Analysis of optimum location for the
additional STs for the model regions④ Proposal of the guideline of locating STs
5

Traffic simulator for analyzing charging ST of next
generation vehicle:「EV-OLYENTOR@」
Pre-post tool
Map Database(Digital Map25000,
etc)
Traffic Data
(Traffic census etc.)
Census Data
( employee, businessfacility etc.)

Set up input files
for traffic
simulator

Place charging STs
Modify road dataInput traffic jam data
Specify simulation
regionMap and ST location of
Kanagawa 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 results

Traffic simulator

Pre-post tool

Map panel

Output panel

Input panel
Console panel

EV traffic
simulation and ST
optimization

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 ST

7

Model of road network
 Digital Road Map(Japan Digital Road Map Association)
 Digital Map 25000 (Geospatial Information Authority of Japan)、Open Street Map(http://www.openstreetmap.org/) are also available.

Map database

Analysis target：
highway, national rd., major local rd.
Map model

Rd. width more than 13m
Rd. width of 5.5m～13m
8

Rd. gradient based on elevation data

Fig．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 figure)

消費エネルギー
kWh/km0.70000.60000.50000.40000.30000.20000.1000Fig．Simulation model of i-MiEV on CRUISE.

-9

-8

-7

-6

-5

-4

-2

-1

1

2

4

5

7

9
8

道路勾配 ％

100km/h

80km/h

60km/h

90km/h

走行速度70km/h

40km/h

50km/h

20km/h

-0.1000

30km/h0.0000

-3

0

3

6

Fig．Mileage function applied in traffic simulation10

Algorithm of charging behavior and
EV conditions
Set origin and destination

EV conditions
Set route between OD

Battery capacity
Start from origin or ST

Under thresholdfor warning sign

No

Initial state of charge 𝑆𝑆𝑆𝑖𝑠𝑠𝑠𝑠𝑠

ST is nearer thandestination

yes
Set route to ST and go to ST, andrecharge at the ST

16
1.0

Fuel mileage 𝐿𝑓𝑓 (km/kWh)

7.5

Threshold for warning sign 𝐸𝑎𝑎𝑎𝑎𝑎(kWh)８.0

Accessary poweryesNo𝐶𝑖 (kWh)value

velocity

𝑃𝑎𝑎 (kW)normal rd./hwy. (km/h)

Traffic jam effect

3.0

45/80
on

Set route from ST to destination

Arrive at the destination

EV starts the charging behavior tonearest ST just after warning sign on
11

Threshold of battery energy forwarning sign SOC≦50％ starts the charging
behavior (Fig2-7、Guideline of
installing charging infrastructure toparking area, 2012, Ministry of Land,
Infrastructure, Transport and Tourism)

 SOC=50％～60％ at the start ofcharging is maximum.
(http://avt.inl.gov/evproject.shtml)

 SOC≦50％ is applied as thetrigger to the charging behavior.
 However, it is said that learning
EV operation enables to makesmall the trigger of SOC for
charging behavior.

Fig： condition of charging behavior
in 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 survey
Probability of trip frequency

1.0

0.8

0.6

0.4

0.2

0.0
1

Fig．Distribution of trip length
Ref.：Disclosed data from JARI to METI obtained in the standardization
project of basic technology for integrated battery system (New Energy
Promotion Council)

2

3
4
5
Trip frequency per travel

6
7(trips)8

Fig．Distribution of trip frequency
Ref. The investigation committee for transportation system
for Imabari city

13

Validation of traffic simulation 1
 Simulation results(simulation) is compared withperson trip survey(survey) for validation
Kawasaki

29
28
27
25
26

Yokohama
24

23
22

14

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

Kawasaki

29
28
27
25
26

Yokohama
24

23
22

15

Algorithm for relocation of ST
Charging ST
moves to charging
demand area

Layer ２
Charging ST Layer

Update the layout ofcharging STs

Layer１
ＥＶ LayerAnalysis layer where charging
STs determine the layout
autonomously according tocharging demand×Mapping the charging demand(location of dead EVs andwarning sign on )Analysis layer where
EV traffic simulation iscarried out with STs
16

Demo. of relocation of ST

17

Algorithm for optimizing layout of STs①②③

Analyze the locate of warning sign on and the total
elapsed time of warning sign by traffic simulation
According the ranking of the elapsed time of warning signfor each city, charging ST is tentatively placed
Charging STs relocate autonomously according to thecharging demand of warning sign on

×：point where warning sign of dead EV becomes on

Kawasaki：
3rd Charging demand

Kanagawa pref.

Odawara：2nd Charging demand

Yokohama：
1st Charging demand①EV traffic simulation②ST is tentatively placed
according to the ranking③STs relocate
autonomously according
to the charging demand18What is the optimum layout ofcharging STs ?
How many STs are required ?
The point is two viewpoints from EV
user 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 ST
number, 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 fuel：84420(JAF
http://www.jaf.or.jp/rservice/data/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 condition
similar to the present owner-driven car exclusive of several
prefectures

 In this report, the target risk of empty electric energy for EV
is considered as less than 1%, and the required No. of ST is
analyzed.
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 usage
Notice : 1% of dead EV ration is just a milestone, and it should be
reduced more by improving EV performance and ST placement

 Placement to area of large charge demand based on traffic
simulation without the present ST
Notice 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 cities
 Geographical 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-Shizuoka
prefs. case23

Tokyo-Kanagawa-Shizuoka prefs. case
①Metro area
Tokyo・Kawasaki・Yokohama②Major road
Tomei Highway③Major city
Hamamtsu・Shizuoka④Peninsula area
Izu peninsula24

Tottori and Okayama prefs. case
④Major city
Yonago・Kurayoshi・Tottori③Highland area
Daisen④ Major road
National Rd. 53・482②Major city
Tuyama・Mimasaka② Major roadHwy. of Okayama・chugoku・Yonago① Metro area
Okayama・Kurashiki25

Osaka pref. case

② Highland area
Northern Minou area

① Metro area
Osaka

② Highland area
Kawachinagano

26

Gifu and Aichi prefs. case②Major city
Takayama③ Major road
Tokaihokuriku Hwy.

① Metro area
Gifu, Nagoya, Toyohashi⑤ Highland area
Norikura・Okuhida③ Major road
National Rd. 41② Major road
Chuo Hwy.

④ Highland area
Eastern Aichi area
④Peninsula area
Chita, Atsumi
27

Tochigi pref. case
③ Highland area
Kanuma・Nikko・Nasu④Major city
Nasukarasuyama・Otawara① Major road
Tohoku Hwy.
Nikko-Utsunomiya Rd.

②Major city
Utsunomiya②Major city
Ashikaga・Oyama

28

Aomori pref. case
①Major city
Mutsu
④Peninsula area
Tsugaru①Major city
Aomori③ Major road
National Rd. 279①Major city
Hirosaki・Goshogawara② Major road
Michinoku Rd.
National Rd. 4・279④ Highland area
Shirakami③ Highland area
Hakkoda①Major city
Hachinohe・Towada

29

Procedure of ST placement
Pattern of ST placement based on theanalysis results①Preferential placement in grid-like fashion to
the Metro area
②Placement to the major city
③Placement to the major road between major
cities④Placement to peninsula and highland area⑤
Placement 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 its
value more than 100, those cities arecorresponding to the preferential placement area31

東京都23区
八王子市
立川市
武蔵野市
三鷹市
青梅市
府中市
昭島市
調布市
町田市
小金井市
小平市
日野市
東村山市
国分寺市
国立市
福生市
狛江市
東大和市
清瀬市
東久留米市
武蔵村山市
多摩市
稲城市
羽村市
あきる野市
西東京市
横浜市
川崎市
横須賀市
平塚市
鎌倉市
藤沢市
小田原市
茅ヶ崎市
逗子市
相模原市
三浦市
秦野市
厚木市
大和市
伊勢原市
海老名市
座間市
南足柄市
綾瀬市
静岡市
浜松市
沼津市
熱海市
三島市
富士宮市
伊東市
島田市
富士市
磐田市
焼津市
掛川市
藤枝市
御殿場市
袋井市
下田市
裾野市
湖西市
伊豆市
御前崎市
菊川市
伊豆の国市

大都市圏評価指数偏差値

Standard deviation score for all cities
Preferential placementarea：Tokyo 23 wards

180

160

140

120

100

80

60

40

20

0

Fig．Standard deviation score of evaluation index for Tokyo-Kanagawa-Shizuoka prefs.

32

Standard deviation score for all cities
Preferential placementarea：Nagoya city

180

大都市圏評価指数偏差値

160
140
120
100
80
60
40
20

岐阜市
大垣市
高山市
多治見市
関市
中津川市
美濃市
瑞浪市
羽島市
恵那市
美濃加茂市
土岐市
各務原市
可児市
山県市
瑞穂市
飛騨市
本巣市
郡上市
下呂市
海津市
名古屋市
豊橋市
岡崎市
一宮市
瀬戸市
半田市
春日井市
豊川市
津島市
碧南市
刈谷市
豊田市
安城市
西尾市
蒲郡市
犬山市
常滑市
江南市
小牧市
稲沢市
新城市
東海市
大府市
知多市
知立市
尾張旭市
高浜市
岩倉市
豊明市
日進市
田原市
愛西市
清須市

0

Fig. Standard deviation score of evaluation index for Gifu-Aichi prefs.

Preferential placementarea：Okayama & Kurashiki

180

大都市評価指数偏差値

160

Not preferential placementarea：Utsunomiya

140
120
100
80
60
40
20

美作市

真庭市

赤磐市

備前市

瀬戸内市

新見市

高梁市

総社市

井原市

笠岡市

玉野市

津山市

倉敷市

岡山市

境港市

倉吉市

米子市

鳥取市

0

Fig. 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 the
major road required for fuel empty risk less than1%
 Averaged ST spaces along 24 national roads areevaluated according to the optimum ST layout
analysis for fuel empty risk less than 1%
 (ST space) ≦-0.00064×(24hr. traffic volume)＋38.8

34

Upper limit of ST space(ST space [km])≦ -0.00064×(24hr. traffic volume [car])＋38.8

Averaged
ST space (km)(km)
充電ST設置間隔

3530
25Grid-like placement of
5-10km in a metroarea and large cities

20
15
10
5
0
0

20000
40000
24時間交通量(台) (car)
24hr.
Traffic volume

60000

Highland 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 analyzed
prefectures.
 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 proportional
to 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)0.19
36

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) + 0.822
8

充電ST設置数 (箇所)7
6

Takayama

5
4
3

Okayama

2
1
0
0

5000
ST数評価指数

The city which recently
merged with depopulated
10000 area has a tendency to be thelower limit.

Nakatsukawa
37

Summary Analysis on optimization of the layout of charging stations(ST) was carried out by estimating the charging demand of
EV based on the traffic simulation
 As the layout method for STs, the following empiricalmethods are proposed based on the optimum layout
analysis, condition for preferential placement to large city,
space of ST placement along major roads, allocation of ST
for major cities, No. of ST for each prefecture.
 Increase of sample number of the prefecture is required to
improve 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

Acknowledgement
We had kind instruction from and cooperation withthe automobile division of Ministry of economy,
trade and industry, Next-generation vehicle
promotion 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 to
everyone concerned with this study.

39

Thank you for your attention.
If you have any questions orcomments, please contact Hiwatari(hiwatari@criepi.denken.or.jp)40