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An imitation learning-based energy management strategy for electric vehicles considering battery aging

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  • Ye, Yiming
  • Wang, Hanchen
  • Xu, Bin
  • Zhang, Jiangfeng

Abstract

—The benefits of an electrified powertrain system, including increased energy efficiency and decreased emission, have made electrification of powertrain systems a top priority for the automobile industry. There has been a significant advancement in studying state-of-the-art battery technology in electric vehicle applications. However, the performance and longevity of electric vehicles may suffer due to battery degradation during vehicle usage. Additionally, there is a need for additional research on energy saving and battery degradation in hybrid energy storage systems for electric vehicles equipped with batteries and supercapacitors. This paper proposes an imitation Q-learning-based energy management system designed to improve energy efficiency and reduce battery degradation for the battery and supercapacitor electric vehicle. A battery electric vehicle is also studied for comparison purposes. To test the efficacy of the proposed method, experiments are conducted using a motor-generator set and a dSPACE SCALEXIO system. The comparisons indicate that the battery degradation is reduced by 26.36% and energy efficiency is increased by 3.83% through the imitation Q-learning energy management strategy.

Suggested Citation

  • Ye, Yiming & Wang, Hanchen & Xu, Bin & Zhang, Jiangfeng, 2023. "An imitation learning-based energy management strategy for electric vehicles considering battery aging," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422301931x
    DOI: 10.1016/j.energy.2023.128537
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    1. Juan Carlos Paredes-Rojas & Ramón Costa-Castelló & Rubén Vázquez-Medina & Juan Alejandro Flores-Campos & Christopher Rene Torres-San Miguel, 2025. "Experimental Study on Using Biodiesel in Hybrid Electric Vehicles," Energies, MDPI, vol. 18(7), pages 1-22, March.
    2. Liu, Weirong & Yao, Pengfei & Wu, Yue & Duan, Lijun & Li, Heng & Peng, Jun, 2025. "Imitation reinforcement learning energy management for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 378(PA).
    3. Wenna Xu & Hao Huang & Chun Wang & Shuai Xia & Xinmei Gao, 2025. "A Comparative Study of Energy Management Strategies for Battery-Ultracapacitor Electric Vehicles Based on Different Deep Reinforcement Learning Methods," Energies, MDPI, vol. 18(5), pages 1-18, March.
    4. Sun, Zhicheng & Hu, Jianjun & Yao, Zutang & Xue, Shouzhi, 2024. "Optimization of electromagnetic vibration for integrated electric drive systems based on electric vehicle driving cycle considering energy consumption," Energy, Elsevier, vol. 313(C).
    5. Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).
    6. Guan, Kaifu & Huang, Zhiwu & Gao, Yang & Wu, Yue & Li, Fei & Li, Heng, 2025. "Towards adaptive deep reinforcement learning energy management for electric vehicles: An online updating approach," Energy, Elsevier, vol. 325(C).

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