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Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information

Author

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  • Jia, Chunchun
  • Zhou, Jiaming
  • He, Hongwen
  • Li, Jianwei
  • Wei, Zhongbao
  • Li, Kunang

Abstract

The escalating level of vehicle electrification and intelligence makes higher requirements for the energy management strategy (EMS) of fuel cell vehicles. Environmental and road conditions can significantly influence the power demand of the load, thereby affecting the lifespan and efficiency of vehicular energy systems. To ensure that the vehicle is always in optimal working condition, this study innovatively proposes a health-conscious EMS framework based on twin delayed deep deterministic policy gradient (TD3) algorithm for fuel cell hybrid electric bus (FCHEB). First, the environment and look-ahead road information obtained through vehicle sensors, GPS and Geographic Information System is used to establish the energy management problem formulation. Secondly, a TD3-based data-driven EMS is developed with the objective of optimizing hydrogen fuel economy, fuel cell durability and battery thermal health status. Finally, the strategy validation is performed in a developed validation environment that contains terrain information, ambient temperature, and real-world collected driving conditions. The validation results indicate that compared to the state-of-the-art TD3-based EMS, the proposed EMS can improve battery life by 28.02 % and overall vehicle economy by 8.92 %.

Suggested Citation

  • Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035405
    DOI: 10.1016/j.energy.2023.130146
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    References listed on IDEAS

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    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    2. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    3. Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
    4. Lin, Xinyou & Xu, Xinhao & Wang, Zhaorui, 2022. "Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle," Applied Energy, Elsevier, vol. 321(C).
    5. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
    6. Ma, Suhui & Qin, Yanzhou & Liu, Yuwen & Sun, Liancheng & Guo, Qiaoyu & Yin, Yan, 2022. "Delamination evolution of PEM fuel cell membrane/CL interface under asymmetric RH cycling and CL crack location," Applied Energy, Elsevier, vol. 310(C).
    7. Ajanovic, A. & Glatt, A. & Haas, R., 2021. "Prospects and impediments for hydrogen fuel cell buses," Energy, Elsevier, vol. 235(C).
    8. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    9. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
    10. Zhang, Kaixuan & Ruan, Jiageng & Li, Tongyang & Cui, Hanghang & Wu, Changcheng, 2023. "The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus," Energy, Elsevier, vol. 269(C).
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