IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v391y2025ics0306261925006208.html

A comparative study of multi-objective and neuroevolutionary-based reinforcement learning algorithms for optimizing electric vehicle charging and load management

Author

Listed:
  • Kemper, Neele
  • Heider, Michael
  • Pietruschka, Dirk
  • Hähner, Jörg

Abstract

The electrification of transportation requires the development of smart charging management systems for electric vehicles to optimize grid performance and enhance user satisfaction. However, existing methods often reduce multi-objective problems to single-objective formulations, limiting their ability to balance conflicting objectives and requiring iterative runs for diverse solutions.

Suggested Citation

  • Kemper, Neele & Heider, Michael & Pietruschka, Dirk & Hähner, Jörg, 2025. "A comparative study of multi-objective and neuroevolutionary-based reinforcement learning algorithms for optimizing electric vehicle charging and load management," Applied Energy, Elsevier, vol. 391(C).
  • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006208
    DOI: 10.1016/j.apenergy.2025.125890
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925006208
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.125890?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    2. Qiu, Dawei & Wang, Yi & Hua, Weiqi & Strbac, Goran, 2023. "Reinforcement learning for electric vehicle applications in power systems:A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    3. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    5. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
    2. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2025. "Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications," Energies, MDPI, vol. 18(19), pages 1-50, October.
    3. Liu, Yiwei & Tang, Yinggan & Hua, Changchun, 2025. "Multi-objective nutcracker optimization algorithm based on fast non-dominated sorting and elite strategy for grid-connected hybrid microgrid system scheduling," Renewable Energy, Elsevier, vol. 242(C).
    4. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    5. Mahuze, Richard A. & Amadeh, Ali & Yuan, Bo & Zhang, K. Max, 2025. "Collaborative optimization framework for capacity planning of a prosumer-based peer-to-peer electricity trading community," Applied Energy, Elsevier, vol. 384(C).
    6. Imed Khabbouchi & Dhaou Said & Aziz Oukaira & Idir Mellal & Lyes Khoukhi, 2023. "Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)," Energies, MDPI, vol. 16(5), pages 1-15, February.
    7. He, Wangli & Li, Chengyuan & Cai, Chenhao & Qing, Xiangyun & Du, Wenli, 2024. "Suppressing active power fluctuations at PCC in grid-connection microgrids via multiple BESSs: A collaborative multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 373(C).
    8. Sørensen, Åse Lekang & Ludvigsen, Bjørn & Andresen, Inger, 2023. "Grid-connected cabin preheating of Electric Vehicles in cold climates – A non-flexible share of the EV energy use," Applied Energy, Elsevier, vol. 341(C).
    9. Li, Yujing & Zhang, Zhisheng & Xing, Qiang, 2025. "Real-time online charging control of electric vehicle charging station based on a multi-agent deep reinforcement learning," Energy, Elsevier, vol. 319(C).
    10. Wang, Wenwei & Zhao, Wentao & Zhou, Xingyu & Zhang, Xinyong & Wu, Wentao & Liu, Manyu, 2025. "Deep learning-aided stochastic integrated optimization of highway service area renewable energy systems adopting a novel topology," Energy, Elsevier, vol. 338(C).
    11. Powell, Siobhan & Martin, Sonia & Rajagopal, Ram & Azevedo, Inês M.L. & de Chalendar, Jacques, 2024. "Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals," Energy Policy, Elsevier, vol. 190(C).
    12. Zhang, Hao & Lei, Nuo & Chen, Boli & Li, Bingbing & Li, Rulong & Wang, Zhi, 2024. "Modeling and control system optimization for electrified vehicles: A data-driven approach," Energy, Elsevier, vol. 310(C).
    13. Yang, Hanqian & Zhou, Lefeng & Kang, Yuelin & Wang, Zicong & Liang, Jichao & Zhang, Fang, 2025. "Simplified-road-condition-based global optimization and calibration strategy for PHEV energy management," Energy, Elsevier, vol. 329(C).
    14. Xu, Hairun & Zhang, Ao & Wang, Qingle & Hu, Yang & Fang, Fang & Cheng, Long, 2025. "Quantum Reinforcement Learning for real-time optimization in Electric Vehicle charging systems," Applied Energy, Elsevier, vol. 383(C).
    15. Yong Fang & Minghao Li & Yunli Yue & Zhonghua Liu, 2024. "Two-Tier Configuration Model for the Optimization of Enterprise Costs and User Satisfaction for Rural Microgrids," Mathematics, MDPI, vol. 12(20), pages 1-19, October.
    16. Kahil, Hussain & Sharma, Shiva & Välisuo, Petri & Elmusrati, Mohammed, 2025. "Reinforcement learning for data center energy efficiency optimization: A systematic literature review and research roadmap," Applied Energy, Elsevier, vol. 389(C).
    17. Azimian, Mahdi & Shen, Xinwei & Gharehpetian, Gevork B., 2025. "Robust scenario-based stochastic expansion planning of multi-carrier microgrids considering incentive-based loans," Applied Energy, Elsevier, vol. 401(PA).
    18. Yu, Gang & Ye, Xianming & Gong, Dunwei & Xia, Xiaohua, 2025. "Stochastic planning for transition from shopping mall parking lots to electric vehicle charging stations," Applied Energy, Elsevier, vol. 379(C).
    19. Rehman, Anis Ur & Lu, Junwei & Du, Bo & Bai, Feifei & Sanjari, Mohammad J. & Hossain, Md Alamgir, 2026. "Vehicle-to-grid technology for load balancing and energy management: A comprehensive review of technical, economic and environmental perspectives," Applied Energy, Elsevier, vol. 402(PB).
    20. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.