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Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle

Author

Listed:
  • Xiao, Boyi
  • Yang, Weiwei
  • Wu, Jiamin
  • Walker, Paul D.
  • Zhang, Nong

Abstract

The modern energy management strategy (EMS) plays a vital role in the energy efficiency of the extended range electric vehicle. However, some modern strategies such as model predictive control (MPC) and dynamic programming (DP) have limited practical potential because they are subject to the pre-known environment information and noise interference. The reinforcement learning (RL)control strategy can be adopted as online control to interact with the vehicle and the environment. In this study, a novel auxiliary power unit (APU) charging strategy with multi-object optimization is proposed to achieve high fuel conversion efficiency while maintaining battery charging health. The state-of-the-art algorithm, Soft Actor-Critic (SAC), is applied to achieve better exploration of the possible APU behaviour and solve the sensitivity and poor convergence problems from the current RL studies. Its performance is further verified by the results of the Deep Deterministic Policy Gradient (DDPG) algorithm and DP. Three innovative targets are selected as the RL rewards for optimization: the engine fuel rate, SOC charging trajectory, and the battery charging rate (C-rate). The first adoption of the battery C-rate monitoring in RL-based energy management strategy helps extend the battery lifespan from excessive discharge. The comparative results show that the SAC had a 36% faster convergence speed than DDPG while providing a smoother and more stable action space. The fuel consumption with SAC also outplays that of DDPG by around 3%, which achieves almost 95% of the global optimization result. The successful deployment of the SAC algorithm as an EMS indicates its standout ability in dealing with wide-range actions and states with high randomness, revealing the practical potential compared with the existing RL strategies.

Suggested Citation

  • Xiao, Boyi & Yang, Weiwei & Wu, Jiamin & Walker, Paul D. & Zhang, Nong, 2022. "Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle," Energy, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010088
    DOI: 10.1016/j.energy.2022.124105
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    References listed on IDEAS

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    1. Jen-Chiun Guan & Bo-Chiuan Chen & Yuh-Yih Wu, 2019. "Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles," Energies, MDPI, vol. 12(9), pages 1-24, 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. Liu, Teng & Wang, Bo & Yang, Chenglang, 2018. "Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning," Energy, Elsevier, vol. 160(C), pages 544-555.
    5. 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.
    6. Han, Xuefeng & He, Hongwen & Wu, Jingda & Peng, Jiankun & Li, Yuecheng, 2019. "Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 254(C).
    7. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    9. Zuo, Hongyan & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2022. "Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation," Energy, Elsevier, vol. 238(PB).
    10. 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.
    11. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Yang, Xiaofeng & He, Hongwen & Wei, Zhongbao & Wang, Rui & Xu, Ke & Zhang, Dong, 2023. "Enabling Safety-Enhanced fast charging of electric vehicles via soft actor Critic-Lagrange DRL algorithm in a Cyber-Physical system," Applied Energy, Elsevier, vol. 329(C).
    2. Liang, Zhaowen & Ruan, Jiageng & Wang, Zhenpo & Liu, Kai & Li, Bin, 2024. "Soft actor-critic-based EMS design for dual motor battery electric bus," Energy, Elsevier, vol. 288(C).
    3. Kunyu Wang & Rong Yang & Yongjian Zhou & Wei Huang & Song Zhang, 2022. "Design and Improvement of SD3-Based Energy Management Strategy for a Hybrid Electric Urban Bus," Energies, MDPI, vol. 15(16), pages 1-21, August.
    4. He, Hongwen & Su, Qicong & Huang, Ruchen & Niu, Zegong, 2024. "Enabling intelligent transferable energy management of series hybrid electric tracked vehicle across motion dimensions via soft actor-critic algorithm," Energy, Elsevier, vol. 294(C).

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