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An experimentally validated health-aware hierarchical energy management strategy for fuel cell heavy-duty trucks

Author

Listed:
  • Wang, Han
  • Zhu, Dan
  • Chen, Bojun
  • Ren, Yuyue
  • Ning, Shichao
  • Zhao, Xuan

Abstract

Fuel cell heavy-duty trucks (FCHTs) are considered a key pathway toward zero-emission freight transport. To achieve their large-scale deployment, there are two critical challenges: rapid fuel cell degradation and high overall energy consumption. Addressing these issues requires an advanced energy management strategy (EMS) capable of balancing efficiency and durability under real-world driving conditions. In this study, a high-power hydrogen fuel cell system model is established, which incorporates the degradation characteristics of both fuel cell and battery. Based on this, a health-aware hierarchical EMS (HH-EMS) with a two-layer fuzzy control structure is proposed to jointly optimize energy source-durability and vehicle energy consumption. The first control layer ensures real-time optimal power distribution among the energy sources, enhancing supply flexibility and adaptability. The second layer dynamically adjusts power allocation based on the current state of health (SOH) of the energy sources to extend the lifespan of the energy system. Moreover, the proposed strategy adopts equivalent hydrogen consumption, which accounts for the additional hydrogen loss caused by fuel cell degradation, as the optimization objective. A genetic algorithm (GA) is employed to optimize the parameters of the fuzzy control system, enabling the determination of an optimal EMS solution. The results of simulation and vehicle tests in 6 cycles reveal significant improvements over conventional strategies, achieving a reduction of 36.7% in fuel cell degradation and a decrease of 25.01% in hydrogen consumption, effectively enhancing overall system longevity and operational efficiency.

Suggested Citation

  • Wang, Han & Zhu, Dan & Chen, Bojun & Ren, Yuyue & Ning, Shichao & Zhao, Xuan, 2025. "An experimentally validated health-aware hierarchical energy management strategy for fuel cell heavy-duty trucks," Energy, Elsevier, vol. 341(C).
  • Handle: RePEc:eee:energy:v:341:y:2025:i:c:s0360544225050583
    DOI: 10.1016/j.energy.2025.139416
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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, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    2. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    3. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Wang, Yichun & Zhang, Yuanzhi & Zhang, Caizhi & Zhou, Jiaming & Hu, Donghai & Yi, Fengyan & Fan, Zhixian & Zeng, Tao, 2023. "Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition," Energy, Elsevier, vol. 263(PF).
    5. Quan, Shengwei & He, Hongwen & Chen, Jinzhou & Zhang, Zhendong & Han, Ruoyan & Wang, Ya-Xiong, 2023. "Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method," Energy, Elsevier, vol. 278(PA).
    6. Li, Bing & Wan, Kechuang & Xie, Meng & Chu, Tiankuo & Wang, Xiaolei & Li, Xiang & Yang, Daijun & Ming, Pingwen & Zhang, Cunman, 2022. "Durability degradation mechanism and consistency analysis for proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 314(C).
    7. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    8. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    9. Song, Ke & Ding, Yuhang & Hu, Xiao & Xu, Hongjie & Wang, Yimin & Cao, Jing, 2021. "Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle," Applied Energy, Elsevier, vol. 285(C).
    10. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    11. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
    12. 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).
    13. Liu, Zhao & Chen, Huicui & Zhang, Tong, 2022. "Review on system mitigation strategies for start-stop degradation of automotive proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 327(C).
    14. 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).
    15. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
    16. Sun, Zhendong & Wang, Yujie & Chen, Zonghai & Li, Xiyun, 2020. "Min-max game based energy management strategy for fuel cell/supercapacitor hybrid electric vehicles," Applied Energy, Elsevier, vol. 267(C).
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