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A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning

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  • Zhou, Jianhao
  • Xue, Siwu
  • Xue, Yuan
  • Liao, Yuhui
  • Liu, Jun
  • Zhao, Wanzhong

Abstract

The formulation of high-efficient energy management strategy (EMS) for hybrid electric vehicles (HEVs) becomes the most crucial task owing to the variation of electrified powertrain topology and uncertainty of driving scenarios. In this study, a deep reinforcement learning (DRL) algorithm, namely TD3, is leveraged to derivate intelligent EMS for HEV. A heuristic rule-based local controller (LC) is embedded within the DRL loop to eliminate irrational torque allocation with considering the characteristics of powertrain components. In order to resolve the influence of environmental disturbance, a hybrid experience replay (HER) method is proposed based on a mixed experience buffer (MEB) consisting of offline computed optimal experience and online learned experience. The results indicate that improved TD3 based EMS obtained the best fuel optimality, fastest convergence speed and highest robustness in comparison to typical value-based and policy-based DRL EMSs under various driving cycles. LC leads to a boosting effect on the convergence speed of TD3-based EMS wherein a “warm” start of exploring is exhibited. Meanwhile, by incorporating HER coupled with MEB, the impact of environmental disturbance including load mass and road gradient, as an increase of input observations, can be negligible to the performance of TD3-based EMS.

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  • Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221003674
    DOI: 10.1016/j.energy.2021.120118
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    1. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
    2. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    4. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    5. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    6. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    7. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    8. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    10. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    11. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
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    Cited by:

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    16. Wang, Hanchen & Ye, Yiming & Zhang, Jiangfeng & Xu, Bin, 2023. "A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle," Energy, Elsevier, vol. 266(C).
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    18. 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.
    19. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    20. Wei, Hongqian & Ai, Qiang & Zhao, Wenqiang & Zhang, Youtong, 2022. "Modelling and experimental validation of an EV torque distribution strategy towards active safety and energy efficiency," Energy, Elsevier, vol. 239(PA).
    21. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
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    23. García, Antonio & Monsalve-Serrano, Javier & Lago Sari, Rafael & Martinez-Boggio, Santiago, 2022. "Energy assessment of an electrically heated catalyst in a hybrid RCCI truck," Energy, Elsevier, vol. 238(PA).

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