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Hierarchical Charging Scheduling Strategy for Electric Vehicles Based on NSGA-II

Author

Listed:
  • Yikang Chen

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Zhicheng Bao

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Yihang Tan

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Jiayang Wang

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Yang Liu

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Haixiang Sang

    (College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Xinmei Yuan

    (College of Automotive Engineering, Jilin University, Changchun 130025, China
    China National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China)

Abstract

Electric vehicles (EVs) are gradually gaining high penetration in transportation due to their low carbon emissions and high power conversion efficiency. However, the large-scale charging demand poses significant challenges to grid stability, particularly the risk of transformer overload caused by random charging. It is necessary that a coordinated charging strategy be carried out to alleviate this challenge. We propose a hierarchical charging scheduling framework to optimize EV charging consisting of demand prediction and hierarchical scheduling. Fuzzy reasoning is introduced to predict EV charging demand, better modeling the relationship between travel distance and charging demand. A hierarchical model was developed based on NSGA-II, where the upper layer generates Pareto-optimal power allocations and then the lower layer dispatches individual vehicles under these allocations. A simulation under this strategy was conducted in a residential scenario. The results revealed that the coordinated strategy reduced the user costs by 21% and the grid load variance by 64% compared with uncoordinated charging. Additionally, the Pareto front could serve as a decision-making tool for balancing user economic interest and grid stability objectives.

Suggested Citation

  • Yikang Chen & Zhicheng Bao & Yihang Tan & Jiayang Wang & Yang Liu & Haixiang Sang & Xinmei Yuan, 2025. "Hierarchical Charging Scheduling Strategy for Electric Vehicles Based on NSGA-II," Energies, MDPI, vol. 18(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3269-:d:1684906
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    References listed on IDEAS

    as
    1. Shang, Yitong & Li, Duo & Li, Yang & Li, Sen, 2025. "Explainable spatiotemporal multi-task learning for electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 384(C).
    2. Mohamed Mokhtar & Mostafa F. Shaaban & Mahmoud H. Ismail & Hatem F. Sindi & Muhyaddin Rawa, 2022. "Reliability Assessment under High Penetration of EVs including V2G Strategy," Energies, MDPI, vol. 15(4), pages 1-17, February.
    3. Emilia M. Szumska, 2023. "Electric Vehicle Charging Infrastructure along Highways in the EU," Energies, MDPI, vol. 16(2), pages 1-18, January.
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    Cited by:

    1. Syed Abdullah Al Nahid & Junjian Qi, 2025. "A Hybrid EV Charging Approach Based on MILP and a Genetic Algorithm," Energies, MDPI, vol. 18(14), pages 1-30, July.

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