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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1488234
This article is part of the Research Topic Distributed Learning, Optimization, and Control Methods for Future Power Grids, Volume II View all 18 articles

Battery Swapping Scheduling for Electric Vehicles: A Non-cooperative Game Approach

Provisionally accepted
Yu Zhang Yu Zhang 1Tao Han Tao Han 1Wei He Wei He 1Jianhua Xia Jianhua Xia 2Lichao Cui Lichao Cui 2Zuofu Ma Zuofu Ma 2Shiwei Liu Shiwei Liu 3*
  • 1 Three Gorges Electric Power Co., Ltd., Wuhan, China
  • 2 China Yangtze Power Co., Ltd., Beijing, China
  • 3 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

The final, formatted version of the article will be published soon.

    In recent years, electric vehicle (EV) battery-swapping technology has rapidly evolved and is expected to become widely prevalent shortly. Therefore, it is crucial to develop efficient battery-swapping scheduling algorithms to optimize the operations of battery-swapping systems. This paper proposes a non-cooperative game approach for the battery-swapping scheduling of EVs. To reduce the waiting time for battery swapping and improve the scheduling efficiency of EVs, a swapping process model inspired by the job-shop scheduling problem is proposed, and the cost function of each EV comprehensively considers the travel time, waiting time, and battery swapping price. To capture the competitive relationship among EVs, a non-cooperative game model for battery swapping scheduling is established considering the finite quantities of batteries and swapping grippers. To find the pure strategy Nash equilibrium, an iterative best response algorithm is developed, satisfying constraints including those couple decisions of different EVs. Case studies demonstrate the fairness and scheduling efficiency of the proposed approach.

    Keywords: Battery swapping, electric vehicle, integer programming, Non-cooperative game, transportation electrification

    Received: 29 Aug 2024; Accepted: 01 Nov 2024.

    Copyright: © 2024 Zhang, Han, He, Xia, Cui, Ma and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shiwei Liu, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

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