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A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle

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  • Zou, Weitao
  • Li, Jianwei
  • Yang, Qingqing
  • Wan, Xinming
  • He, Yuntang
  • Lan, Hao

Abstract

Fuel cell longevity management is the key issue for the vehicle application as it is extremely susceptible to operating circumstances. Hybridizing the fuel cells with the power batteries forming a fuel cell hybrid electric vehicle (FCHEV) is prospective solution. However, the lifetime competition mechanism existing between the two different power sources makes it difficult to balance the system lifetime economy in real-time against driving conditions. Therefore, this paper proposes a new discrete optimization approach derived from min–max game theory to describe the mapping relationship between the output power of the power source with the complicated operating conditions so as to realize the real-time aging quantification of both the fuel cell and batteries in FCHEVs. Meanwhile, under the proposed discrete optimization method, the degradation interaction between fuel cell and battery is well established to decouple the lifetime competition between the two power sources with the consideration of system economy. In addition, an empirical-data-driven battery lifetime real-time model is developed to describe the battery degradation regarding its life stages. The real-time performance of the proposed aging model, as well as the discrete optimization approach, is verified by hardware in the loop experiment benefiting in both fuel cell and battery degradation reduction and energy consumption economy.

Suggested Citation

  • Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000314
    DOI: 10.1016/j.apenergy.2023.120667
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    Cited by:

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    2. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).

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