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The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution

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  • Cui, Shaohua
  • Yao, Baozhen
  • Chen, Gang
  • Zhu, Chao
  • Yu, Bin

Abstract

A novel charging approach, named mobile charging service, is proposed for battery electric vehicles. The mobile charging service operator receives booking reservations from battery electric vehicle drivers including electricity, locations and required time windows, and based on the information allocated mobile charging vehicles do the charging. This service offers an alternative of fixed charging facilities and also flexibility to battery electric vehicle drivers as they do not need to wait in their vehicles during the charging service. Mobile charging service operators may be equipped with high service-efficiency mobile charging vehicles and low service-efficiency mobile charging vehicles. High service-efficiency mobile charging vehicles can complete the same charging service in a short period of time and charge high service fees, but the cost is high, while low service-efficiency mobile charging vehicles are the opposite. It is urgent to optimize both the number and routes of these two types of mobile charging vehicles at the same time to take advantage of their respective advantages from the operator’s perspective. To address this challenge, we introduce the mobile charging service problem with time windows and multiple mode service, and formulate it as a 0–1 mixed integer linear model. The designed model is tested in 24 small examples and two large instances in Dalian, China, where sensitivity analyses are performed to further evaluate the effectiveness of this model. The result shows, the combination of mobile charging vehicles with different service efficiency can help the operator obtain more profits than all mobile charging vehicles with the same service efficiency regardless of high or low service efficiency.

Suggested Citation

  • Cui, Shaohua & Yao, Baozhen & Chen, Gang & Zhu, Chao & Yu, Bin, 2020. "The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304096
    DOI: 10.1016/j.energy.2020.117302
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    Cited by:

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    2. Yu, Bin & Zhou, Huixin & Wang, Lin & Wang, Zirui & Cui, Shaohua, 2021. "An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    3. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    4. Yan Bao & Fangyu Chang & Jinkai Shi & Pengcheng Yin & Weige Zhang & David Wenzhong Gao, 2022. "An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory," Energies, MDPI, vol. 15(14), pages 1-20, July.
    5. Cui, Shaohua & Ma, Xiaolei & Zhang, Mingheng & Yu, Bin & Yao, Baozhen, 2022. "The parallel mobile charging service for free-floating shared electric vehicle clusters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    6. Hedayat Saboori & Shahram Jadid & Mehdi Savaghebi, 2021. "Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption," Energies, MDPI, vol. 14(3), pages 1-19, January.
    7. Afshar, Shahab & Macedo, Pablo & Mohamed, Farog & Disfani, Vahid, 2021. "Mobile charging stations for electric vehicles — A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).

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