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Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function

Author

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  • Fuwu Yan

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China)

  • Jinhai Wang

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China)

  • Changqing Du

    (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China)

  • Min Hua

    (Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK)

Abstract

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

Suggested Citation

  • Fuwu Yan & Jinhai Wang & Changqing Du & Min Hua, 2022. "Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function," Energies, MDPI, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:74-:d:1010373
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    References listed on IDEAS

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    Cited by:

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    2. Hua, Min & Zhang, Cetengfei & Zhang, Fanggang & Li, Zhi & Yu, Xiaoli & Xu, Hongming & Zhou, Quan, 2023. "Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 348(C).
    3. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    4. Zhang, Yahui & Wei, Zeyi & Wang, Zhong & Tian, Yang & Wang, Jizhe & Tian, Zhikun & Xu, Fuguo & Jiao, Xiaohong & Li, Liang & Wen, Guilin, 2024. "Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation," Energy, Elsevier, vol. 292(C).

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