Demand forecasting-based layout planning of electric vehicle charging station locations

Min Li; Wuhong Wang; Hongfei Mu; Xiaobei Jiang; Prakash Ranjitkar; Tao Chen

doi:10.1007/978-981-10-3551-7_81

Demand forecasting-based layout planning of electric vehicle charging station locations

Min Li
, Wuhong Wang^*
, Hongfei Mu
, Xiaobei Jiang
, Prakash Ranjitkar
, Tao Chen

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The growing use of electric vehicles (EVs) promotes environmental protection and energy conservation. The prerequisite in the use of EVs is that they should be adequately charged. The layout planning of charging station locations is therefore a key point in meeting the charging demands of EVs. This study presents three types of charging demands (i.e., conventional charging, fast charging, and fast battery replacement demand) by forecasting electric vehicle ownership with the use of the Bass model based on traditional vehicle development. This model for locating charging stations is built and optimized on the basis of the forecasted charging demands. The aim is to minimize the layout construction cost for charging station locations and the charging cost for customers. A practical example that applies the model to optimize the layout of charging station locations is presented, and the developed model is validated to work effectively. The model provides a theoretical way to optimize the layout of charging station locations and serves as a basis for layout planners and a reference for other researchers.

Original language	English
Title of host publication	Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety
Editors	Wuhong Wang, Xiaobei Jiang, Klaus Bengler, Xiaobei Jiang
Publisher	Springer Verlag
Pages	1009-1021
Number of pages	13
ISBN (Print)	9789811035500
DOIs	https://doi.org/10.1007/978-981-10-3551-7_81
Publication status	Published - 2018
Event	7th International Conference on Green Intelligent Transportation System and Safety, 2016 - Nanjing, China Duration: 1 Jul 2016 → 4 Jul 2016

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	419
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	7th International Conference on Green Intelligent Transportation System and Safety, 2016
Country/Territory	China
City	Nanjing
Period	1/07/16 → 4/07/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Forecasting charging demand
Optimal layout model
Planning of EV charging stations

Access to Document

10.1007/978-981-10-3551-7_81

Cite this

Li, M., Wang, W., Mu, H., Jiang, X., Ranjitkar, P., & Chen, T. (2018). Demand forecasting-based layout planning of electric vehicle charging station locations. In W. Wang, X. Jiang, K. Bengler, & X. Jiang (Eds.), Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety (pp. 1009-1021). (Lecture Notes in Electrical Engineering; Vol. 419). Springer Verlag. https://doi.org/10.1007/978-981-10-3551-7_81

Li, Min ; Wang, Wuhong ; Mu, Hongfei et al. / Demand forecasting-based layout planning of electric vehicle charging station locations. Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety. editor / Wuhong Wang ; Xiaobei Jiang ; Klaus Bengler ; Xiaobei Jiang. Springer Verlag, 2018. pp. 1009-1021 (Lecture Notes in Electrical Engineering).

@inproceedings{068b5e120f1f47218dc63721e0ccc6d1,

title = "Demand forecasting-based layout planning of electric vehicle charging station locations",

abstract = "The growing use of electric vehicles (EVs) promotes environmental protection and energy conservation. The prerequisite in the use of EVs is that they should be adequately charged. The layout planning of charging station locations is therefore a key point in meeting the charging demands of EVs. This study presents three types of charging demands (i.e., conventional charging, fast charging, and fast battery replacement demand) by forecasting electric vehicle ownership with the use of the Bass model based on traditional vehicle development. This model for locating charging stations is built and optimized on the basis of the forecasted charging demands. The aim is to minimize the layout construction cost for charging station locations and the charging cost for customers. A practical example that applies the model to optimize the layout of charging station locations is presented, and the developed model is validated to work effectively. The model provides a theoretical way to optimize the layout of charging station locations and serves as a basis for layout planners and a reference for other researchers.",

keywords = "Forecasting charging demand, Optimal layout model, Planning of EV charging stations",

author = "Min Li and Wuhong Wang and Hongfei Mu and Xiaobei Jiang and Prakash Ranjitkar and Tao Chen",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2018.; 7th International Conference on Green Intelligent Transportation System and Safety, 2016 ; Conference date: 01-07-2016 Through 04-07-2016",

year = "2018",

doi = "10.1007/978-981-10-3551-7\_81",

language = "English",

isbn = "9789811035500",

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}

Li, M, Wang, W, Mu, H, Jiang, X, Ranjitkar, P & Chen, T 2018, Demand forecasting-based layout planning of electric vehicle charging station locations. in W Wang, X Jiang, K Bengler & X Jiang (eds), Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety. Lecture Notes in Electrical Engineering, vol. 419, Springer Verlag, pp. 1009-1021, 7th International Conference on Green Intelligent Transportation System and Safety, 2016, Nanjing, China, 1/07/16. https://doi.org/10.1007/978-981-10-3551-7_81

Demand forecasting-based layout planning of electric vehicle charging station locations. / Li, Min; Wang, Wuhong; Mu, Hongfei et al.
Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety. ed. / Wuhong Wang; Xiaobei Jiang; Klaus Bengler; Xiaobei Jiang. Springer Verlag, 2018. p. 1009-1021 (Lecture Notes in Electrical Engineering; Vol. 419).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Demand forecasting-based layout planning of electric vehicle charging station locations

AU - Li, Min

AU - Wang, Wuhong

AU - Mu, Hongfei

AU - Jiang, Xiaobei

AU - Ranjitkar, Prakash

AU - Chen, Tao

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2018.

PY - 2018

Y1 - 2018

N2 - The growing use of electric vehicles (EVs) promotes environmental protection and energy conservation. The prerequisite in the use of EVs is that they should be adequately charged. The layout planning of charging station locations is therefore a key point in meeting the charging demands of EVs. This study presents three types of charging demands (i.e., conventional charging, fast charging, and fast battery replacement demand) by forecasting electric vehicle ownership with the use of the Bass model based on traditional vehicle development. This model for locating charging stations is built and optimized on the basis of the forecasted charging demands. The aim is to minimize the layout construction cost for charging station locations and the charging cost for customers. A practical example that applies the model to optimize the layout of charging station locations is presented, and the developed model is validated to work effectively. The model provides a theoretical way to optimize the layout of charging station locations and serves as a basis for layout planners and a reference for other researchers.

AB - The growing use of electric vehicles (EVs) promotes environmental protection and energy conservation. The prerequisite in the use of EVs is that they should be adequately charged. The layout planning of charging station locations is therefore a key point in meeting the charging demands of EVs. This study presents three types of charging demands (i.e., conventional charging, fast charging, and fast battery replacement demand) by forecasting electric vehicle ownership with the use of the Bass model based on traditional vehicle development. This model for locating charging stations is built and optimized on the basis of the forecasted charging demands. The aim is to minimize the layout construction cost for charging station locations and the charging cost for customers. A practical example that applies the model to optimize the layout of charging station locations is presented, and the developed model is validated to work effectively. The model provides a theoretical way to optimize the layout of charging station locations and serves as a basis for layout planners and a reference for other researchers.

KW - Forecasting charging demand

KW - Optimal layout model

KW - Planning of EV charging stations

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U2 - 10.1007/978-981-10-3551-7_81

DO - 10.1007/978-981-10-3551-7_81

M3 - Conference contribution

AN - SCOPUS:85026761586

SN - 9789811035500

T3 - Lecture Notes in Electrical Engineering

SP - 1009

EP - 1021

BT - Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety

A2 - Wang, Wuhong

A2 - Jiang, Xiaobei

A2 - Bengler, Klaus

A2 - Jiang, Xiaobei

PB - Springer Verlag

T2 - 7th International Conference on Green Intelligent Transportation System and Safety, 2016

Y2 - 1 July 2016 through 4 July 2016

ER -

Li M, Wang W, Mu H, Jiang X, Ranjitkar P, Chen T. Demand forecasting-based layout planning of electric vehicle charging station locations. In Wang W, Jiang X, Bengler K, Jiang X, editors, Green Intelligent Transportation Systems - Proceedings of the 7th International Conference on Green Intelligent Transportation System and Safety. Springer Verlag. 2018. p. 1009-1021. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-10-3551-7_81

Demand forecasting-based layout planning of electric vehicle charging station locations

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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