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A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms

Author

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  • Kunang Li

    (National Engineering Research Center for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Chunchun Jia

    (National Engineering Research Center for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Xuefeng Han

    (China North Vehicle Research Institute, Beijing 100072, China)

  • Hongwen He

    (National Engineering Research Center for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Energy management strategy (EMS) is critical for improving the economy of hybrid powertrains and the durability of energy sources. In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a fuel cell hybrid electric bus (FCHEB) to optimize the driving cost of the vehicle. First, a TD3-based energy management strategy is established to embed the limits of battery aging and fuel cell power variation into the strategic framework to fully exploit the economic potential of FCHEB. Second, the TD3-based EMS is compared and analyzed with the deep deterministic policy gradient algorithm (DDPG)-based EMS using real-world collected driving conditions as training data. The results show that the TD3-based EMS has 54.69% higher training efficiency, 36.82% higher learning ability, and 2.45% lower overall vehicle operating cost compared to the DDPG-based EMS, validating the effectiveness of the proposed strategy.

Suggested Citation

  • Kunang Li & Chunchun Jia & Xuefeng Han & Hongwen He, 2023. "A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7967-:d:1146052
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    References listed on IDEAS

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    Cited by:

    1. Han, Lijin & You, Congwen & Yang, Ningkang & Liu, Hui & Chen, Ke & Xiang, Changle, 2024. "Adaptive real-time energy management strategy using heuristic search for off-road hybrid electric vehicles," Energy, Elsevier, vol. 304(C).
    2. Yavuz Eray Altun & Osman Akın Kutlar, 2024. "Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles," Energies, MDPI, vol. 17(7), pages 1-39, April.

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