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Operation optimization approaches of electric vehicle battery swapping and charging station: A literature review

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  • Cui, Dingsong
  • Wang, Zhenpo
  • Liu, Peng
  • Wang, Shuo
  • Dorrell, David G.
  • Li, Xiaohui
  • Zhan, Weipeng

Abstract

The battery swapping mode (BSM) for an electric vehicle (EV) is an efficient way of replenishing energy. However, there have been perceived operation-related issues related large-scale deployment of the BSM. However, previous reviews have failed to examine the mathematical methods of the operation optimization process, which are highlighted in this work. The paper aims to provide a complete and systematic overview of the operation optimization approaches for EV battery swapping and charging stations. This work addresses the current operation mode of battery swapping networks and examines the optimization objectives, constraints, and mathematical programming methods. The paper highlights the motivations of different ownership models for establishing different objectives and discusses the merits and drawbacks of approaches in previous studies for different application scenarios. For the possible focus of future work, the paper details opportunities and challenges of dynamic service pricing, battery-to-grid scheduling, and behavior scheduling. This review aids future research of battery charging and swapping station operation and vehicle scheduling, and provides a systematic and theoretical reference for model selection.

Suggested Citation

  • Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Dorrell, David G. & Li, Xiaohui & Zhan, Weipeng, 2023. "Operation optimization approaches of electric vehicle battery swapping and charging station: A literature review," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029814
    DOI: 10.1016/j.energy.2022.126095
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    References listed on IDEAS

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    Cited by:

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    2. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhao, Yiwen & Zhan, Weipeng, 2023. "Stacking regression technology with event profile for electric vehicle fast charging behavior prediction," Applied Energy, Elsevier, vol. 336(C).
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