IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v36y2024i3d10.1007_s10696-023-09508-8.html
   My bibliography  Save this article

Integrated optimization of electric vehicles charging location and allocation for valet charging service

Author

Listed:
  • Xiaoxiao Shen

    (Shanghai Jiao Tong University
    Politecnico di Milano)

  • Jun Lv

    (East China Normal University)

  • Shichang Du

    (Shanghai Jiao Tong University)

  • Yafei Deng

    (Shanghai Jiao Tong University)

  • Molin Liu

    (Shanghai Jiao Tong University)

  • Yulu Zhou

    (Shanghai Jiao Tong University)

Abstract

Since electric vehicles (EVs) have definite benefits over gasoline vehicles, the vehicle market could be dominated by EVs in the future. This paper focuses on the new valet charging service to send staffs to replace users for charging their EVs, which can largely reduce charging anxiety. In this study, the location of charging stations and the allocation of charging demands to charging stations are optimized simultaneously due to the interaction of these decisions. The queueing behavior at the charging station is incorporated into the model, and the average charging waiting time is derived. We construct a mixed integer nonlinear optimization model based on the characteristics of valet charge service and an infinite-source queuing model. The objective is to minimize a total cost of the construction of charging facilities and valet charge service launching (i.e., charging staffs’ road service, round-trip time, and the charging waiting time). The planning problem for valet charging service in this paper contributes to the existing literature on self-charging way where the users of EVs drive at charging stations to recharge their EVs by themselves. An improved genetic algorithm is developed to obtain deployment and operation plans for large-scale instances constructed based a real case in Shanghai. The improved genetic algorithm shows high performance in convergence and solution quality, which provide the service providers an efficient decision support tool. Meaningful managerial insights are also provided, which can help the service provider make better cost-effective design of charging location and allocation plans. For example, the charging station location decisions are not as much as sensitive to critical variables (such as demand level, charging capacities, and the value of time) than the overall cost to those. This means that partial location decisions remain unchanged when the key parameters vary.

Suggested Citation

  • Xiaoxiao Shen & Jun Lv & Shichang Du & Yafei Deng & Molin Liu & Yulu Zhou, 2024. "Integrated optimization of electric vehicles charging location and allocation for valet charging service," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 1080-1106, September.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:3:d:10.1007_s10696-023-09508-8
    DOI: 10.1007/s10696-023-09508-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-023-09508-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-023-09508-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chung, Sung Hoon & Kwon, Changhyun, 2015. "Multi-period planning for electric car charging station locations: A case of Korean Expressways," European Journal of Operational Research, Elsevier, vol. 242(2), pages 677-687.
    2. Yongxi Huang & Shengyin Li & Zhen Qian, 2015. "Optimal Deployment of Alternative Fueling Stations on Transportation Networks Considering Deviation Paths," Networks and Spatial Economics, Springer, vol. 15(1), pages 183-204, March.
    3. Wang, Hua & Meng, Qiang & Wang, Jing & Zhao, De, 2021. "An electric-vehicle corridor model in a dense city with applications to charging location and traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 79-99.
    4. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    5. Rogge, Matthias & van der Hurk, Evelien & Larsen, Allan & Sauer, Dirk Uwe, 2018. "Electric bus fleet size and mix problem with optimization of charging infrastructure," Applied Energy, Elsevier, vol. 211(C), pages 282-295.
    6. Guo, Fang & Zhang, Jingjing & Huang, Zhihong & Huang, Weilai, 2022. "Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation," Energy, Elsevier, vol. 250(C).
    7. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    8. Chen, Zhibin & He, Fang & Yin, Yafeng, 2016. "Optimal deployment of charging lanes for electric vehicles in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 344-365.
    9. Faustino, Fausta J. & Lopes, José Calixto & Melo, Joel D. & Sousa, Thales & Padilha-Feltrin, Antonio & Brito, José A.S. & Garcia, Claudio O., 2023. "Identifying charging zones to allocate public charging stations for electric vehicles," Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    2. Van Can Nguyen & Chi-Tai Wang & Ying-Jiun Hsieh, 2021. "Electrification of Highway Transportation with Solar and Wind Energy," Sustainability, MDPI, vol. 13(10), pages 1-28, May.
    3. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
    4. Sania E. Seilabi & Mohammadhosein Pourgholamali & Mohammad Miralinaghi & Gonçalo Homem de Almeida Correia & Zongzhi Li & Samuel Labi, 2024. "Sustainable Planning of Electric Vehicle Charging Stations: A Bi-Level Optimization Framework for Reducing Vehicular Emissions in Urban Road Networks," Sustainability, MDPI, vol. 17(1), pages 1-23, December.
    5. Liu, Haoxiang & Wang, David Z.W., 2017. "Locating multiple types of charging facilities for battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 30-55.
    6. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    7. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.
    8. Joseph Y. J. Chow & Hamid R. Sayarshad, 2016. "Reference Policies for Non-myopic Sequential Network Design and Timing Problems," Networks and Spatial Economics, Springer, vol. 16(4), pages 1183-1209, December.
    9. Joonho Ko & Tae-Hyoung Tommy Gim & Randall Guensler, 2017. "Locating refuelling stations for alternative fuel vehicles: a review on models and applications," Transport Reviews, Taylor & Francis Journals, vol. 37(5), pages 551-570, September.
    10. Schwerdfeger, Stefan & Bock, Stefan & Boysen, Nils & Briskorn, Dirk, 2022. "Optimizing the electrification of roads with charge-while-drive technology," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1111-1127.
    11. Xiangyu Luo & Rui Qiu, 2020. "Electric Vehicle Charging Station Location towards Sustainable Cities," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
    12. Liu, Haoxiang & Zou, Yuncheng & Chen, Ya & Long, Jiancheng, 2021. "Optimal locations and electricity prices for dynamic wireless charging links of electric vehicles for sustainable transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    13. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2019. "A four-step method for electric-vehicle charging facility deployment in a dense city: An empirical study in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 224-237.
    14. Hernandez, Adrian & Ng, Max & Durango-Cohen, Pablo L. & Mahmassani, Hani S., 2024. "Optimizing service networks to support freight rail decarbonization: Flow selection, facility location, and energy sourcing," European Journal of Operational Research, Elsevier, vol. 317(3), pages 906-920.
    15. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    16. Shaohua Cui & Hui Zhao & Cuiping Zhang, 2018. "Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    17. Praveen Prakash Singh & Fushuan Wen & Ivo Palu & Sulabh Sachan & Sanchari Deb, 2022. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions," Energies, MDPI, vol. 16(1), pages 1-21, December.
    18. Liu, Shaojun & Wang, David Z.W. & Tian, Qingyun & Lin, Yun Hui, 2024. "Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    19. Amit Kumar & Sabyasachee Mishra & Huan Ngo, 2023. "Dynamic Wireless Charging Facility Location Problem for Battery Electric Vehicles under Electricity Constraint," Networks and Spatial Economics, Springer, vol. 23(3), pages 679-713, September.
    20. Lee, Chungmok & Han, Jinil, 2017. "Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 130-152.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:flsman:v:36:y:2024:i:3:d:10.1007_s10696-023-09508-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.