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Study on energy management strategy for hybrid power system with fuel cell hysteresis

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  • Zhao, Xiuliang
  • Yuan, Hehu
  • Wang, Lei
  • Wang, Ruochen
  • Sun, Xiaodong
  • Shi, Dehua
  • Wang, Limei
  • Shikazono, Naoki

Abstract

The voltage hysteresis of fuel cell system (FCS) directly impacts power distribution of the hybrid power system, thereby affecting the economic efficiency of the hybrid power system. Previous research on energy management strategies (EMSs) has often relied on the static model, neglecting the influence of FCS hysteresis on the economics of the hybrid power system. This paper proposes an EMS considering FCS hysteresis to minimize hydrogen consumption. Firstly, a Markov vehicle speed prediction model based on time classification constraint is proposed to accurately predict future speed. Then, the voltage hysteresis of the FCS is analyzed and an equation to characterize the voltage hysteresis is presented. Finally, an equivalent consumption minimum strategy (ECMS) considering fuel cell voltage hysteresis is proposed. The results indicate that the designed EMS can achieve locally optimal output power within the fuel cell system's output range. Compared to the original EMS, the economy improved by 3.55 % under medium-speed conditions and 2.02 % under comprehensive conditions.

Suggested Citation

  • Zhao, Xiuliang & Yuan, Hehu & Wang, Lei & Wang, Ruochen & Sun, Xiaodong & Shi, Dehua & Wang, Limei & Shikazono, Naoki, 2025. "Study on energy management strategy for hybrid power system with fuel cell hysteresis," Energy, Elsevier, vol. 315(C).
  • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544225000490
    DOI: 10.1016/j.energy.2025.134407
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    References listed on IDEAS

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    1. 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).
    2. Jian, Qifei & Zhao, Yang & Wang, Haoting, 2015. "An experimental study of the dynamic behavior of a 2 kW proton exchange membrane fuel cell stack under various loading conditions," Energy, Elsevier, vol. 80(C), pages 740-745.
    3. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    4. Pathapati, P.R. & Xue, X. & Tang, J., 2005. "A new dynamic model for predicting transient phenomena in a PEM fuel cell system," Renewable Energy, Elsevier, vol. 30(1), pages 1-22.
    5. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    6. Tang, Yong & Yuan, Wei & Pan, Minqiang & Li, Zongtao & Chen, Guoqing & Li, Yong, 2010. "Experimental investigation of dynamic performance and transient responses of a kW-class PEM fuel cell stack under various load changes," Applied Energy, Elsevier, vol. 87(4), pages 1410-1417, April.
    7. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    8. 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).
    9. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
    10. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2021. "Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning," Energy, Elsevier, vol. 226(C).
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