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Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy

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  • Lu, Dagang
  • Yi, Fengyan
  • Hu, Donghai
  • Li, Jianwei
  • Yang, Qingqing
  • Wang, Jing

Abstract

The decay process of fuel cell (FC) and battery is highly inconsistent. The existing research focuses on the energy management strategy (EMS) aiming at minimizing the lifespan loss of a single power source. However, this EMS cannot guarantee the optimal overall durability of dual power source systems. Based on this, this paper proposes an EMS for lifespan decay coordination of dual power sources for fuel cell vehicle (FCV). Firstly, a continuous characterization model for FC lifespan decay was developed. Secondly, the influence of control parameters such as FC response speed, filtering order, equivalent consumption minimum strategy (ECMS) equivalent factor on dual power source decay rate is analyzed. Then, the proposed energy management strategy (PEMS) is based on the cooperative control of lifespan decay of two power sources based on the condition identification. Finally, the advantages of PEMS are verified by hardware in-loop simulation. The results show that: under the working conditions of CWC1 and CWC2, compared with ECMS, PEMS reduces the difference of lifespan decay rate of dual power source by 25.62 times and 32.25 times, respectively, and the lifespan decay of fuel cell and power battery tends to be consistent. It is proved that PEMS can realize the decay cooperation of dual power source of FCV under different characteristic working conditions, and significantly improve the overall durability of dual power source system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923008802
    DOI: 10.1016/j.apenergy.2023.121516
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    References listed on IDEAS

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    1. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    2. Yang Shen & Jiaming Zhou & Jinming Zhang & Fengyan Yi & Guofeng Wang & Chaofeng Pan & Wei Guo & Xing Shu, 2023. "Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control," Sustainability, MDPI, vol. 15(16), pages 1-19, August.

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