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Improved artificial hummingbird algorithm for electric vehicle charging station fast and slow charging location determination method

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  • Sixia Fan
  • Xiangyu Zeng
  • Lanxin Li
  • Shuqi Xu

Abstract

Electric vehicle (EV) charging infrastructure is rapidly improving. To address high site selection costs from unbalanced fast-slow charging ratios and multi-party cost allocation issues, we propose a four-objective optimization model based on a three-party cost game (suppliers, users, power grid). The model minimizes: (1) construction/operation costs, (2) user time loss costs, (3) grid power loss costs, and (4) voltage deviation costs under different charging modes. An improved artificial hummingbird algorithm solves the model. Results show the approach improves economic efficiency, provides valuable reference for EV charging station siting, and demonstrates strong algorithm robustness and generalization.

Suggested Citation

  • Sixia Fan & Xiangyu Zeng & Lanxin Li & Shuqi Xu, 2025. "Improved artificial hummingbird algorithm for electric vehicle charging station fast and slow charging location determination method," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-23, September.
    • Handle: RePEc:plo:pone00:0332872
    DOI: 10.1371/journal.pone.0332872
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    References listed on IDEAS

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    1. 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.
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