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Min-max game based energy management strategy for fuel cell/supercapacitor hybrid electric vehicles

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  • Sun, Zhendong
  • Wang, Yujie
  • Chen, Zonghai
  • Li, Xiyun

Abstract

The fuel cell hybrid electric vehicles have received widespread attention in recent years due to their zero-emission and long mileage. However, the durability of the fuel cell declines rapidly under frequent start and stop and power fluctuation. The fuel efficiency of the power system is affected by the power allocation strategy. For low hydrogen consumption and fuel cell durability, an energy management strategy based on the game theory is proposed in this paper, which considers the controller and the future driving condition as both sides of the game to enhance the operational stability in complex driving conditions. The interval forecast for driving conditions is realized by introducing the Markov chain method. To obtain the optimal power allocation strategy in the speed forecast window, it is reasonable to integrate the co-evolutionary algorithm with the game model to leverage its superior ability in solving the constrained min-max problem. In order to verify the robustness and the economic performance of this method, the comparison with other methods is conducted in the typical urban, highway and mixed driving conditions. What is more, the fuel cell degradation and operational stability of four energy management strategies are evaluated by the fuel cell degradation model. The results show that the proposed method can reduce hydrogen consumption by more than 6.82% and avoid fuel cell degradation caused by frequent start and stop and power fluctuation compared with other real-time strategies.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920305985
    DOI: 10.1016/j.apenergy.2020.115086
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Mehroze Iqbal & Amel Benmouna & Frederic Claude & Mohamed Becherif, 2023. "Efficient and Reliable Power-Conditioning Stage for Fuel Cell-Based High-Power Applications," Energies, MDPI, vol. 16(13), pages 1-15, June.
    4. Macias, A. & Kandidayeni, M. & Boulon, L. & Trovão, J.P., 2021. "Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain," Energy, Elsevier, vol. 224(C).
    5. Duan, Hao & Zhang, Caizhi & Wang, Gucheng & Chen, Yu'an & Liu, Zhixiang & Xie, Xianshu & Shuai, Qi, 2022. "Experimental study of the dynamic and transient characteristics of sub-health fuel cell multi-stack systems without DC/DC," Energy, Elsevier, vol. 238(PC).
    6. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    7. 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).
    8. Li, Qi & Wang, Tianhong & Li, Shihan & Chen, Weirong & Liu, Hong & Breaz, Elena & Gao, Fei, 2021. "Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation," Applied Energy, Elsevier, vol. 285(C).
    9. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.
    10. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
    11. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).
    12. Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
    13. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    14. Molina, S. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2021. "Optimization and sizing of a fuel cell range extender vehicle for passenger car applications in driving cycle conditions," Applied Energy, Elsevier, vol. 285(C).
    15. Aissa Benhammou & Hamza Tedjini & Mohammed Amine Hartani & Rania M. Ghoniem & Ali Alahmer, 2023. "Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles," Sustainability, MDPI, vol. 15(13), pages 1-27, June.

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