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Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation

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  • Li, Qi
  • Wang, Tianhong
  • Li, Shihan
  • Chen, Weirong
  • Liu, Hong
  • Breaz, Elena
  • Gao, Fei

Abstract

In order to realize optimal power distribution between proton exchange membrane fuel cell and supercapacitor in hybrid electric tram, an online extremum seeking-based optimized energy management strategy is proposed in this work. Considering that the fuel cell is a complex nonlinear system, its performance will vary as the external parameters change, so it is necessary to consider the performance state of stack. An online extremum seeking algorithm is investigated in this work to seek the maximum power and maximum efficiency points by searching the variation in fuel cell performance. Besides, this work also updates its “safe operating zone” based on the results of the online extremum seeking. This process is achieved by the adaptive recursive least square algorithm. Furthermore, in order to limit the power dynamic of fuel cell, the degradation of the stack is considered in this study. To guarantee the stable and continued operation of the electric tram, the state of charge fluctuation range of supercapacitor is also limited. The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform. The proposed method is also compared with state machine control and equivalent consumption minimization strategy to further demonstrate that it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261921000635
    DOI: 10.1016/j.apenergy.2021.116505
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    References listed on IDEAS

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    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. Zhou, Su & Zhang, Gang & Fan, Lei & Gao, Jianhua & Pei, Fenglai, 2022. "Scenario-oriented stacks allocation optimization for multi-stack fuel cell systems," Applied Energy, Elsevier, vol. 308(C).
    7. Zhang, Gang & Zhou, Su & Gao, Jianhua & Fan, Lei & Lu, Yanda, 2023. "Stacks multi-objective allocation optimization for multi-stack fuel cell systems," Applied Energy, Elsevier, vol. 331(C).
    8. 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.
    9. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    10. Jaekwang Jung & Dongeon Kim & Liyue Yang & Namwook Kim, 2024. "Optimal Energy Management Strategy for Repeat Path Operating Fuel Cell Hybrid Tram," Energies, MDPI, vol. 17(7), pages 1-12, March.
    11. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    12. Martín Antonio Rodríguez Licea & Francisco Javier Pérez Pinal & Allan Giovanni Soriano Sánchez, 2021. "An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids," Energies, MDPI, vol. 14(8), pages 1-23, April.
    13. 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).
    14. Hoai Vu Anh Truong & Hoai An Trinh & Tri Cuong Do & Manh Hung Nguyen & Van Du Phan & Kyoung Kwan Ahn, 2024. "An Enhanced Extremum Seeking-Based Energy Management Strategy with Equivalent State for Hybridized-Electric Tramway-Powered by Fuel Cell–Battery–Supercapacitors," Mathematics, MDPI, vol. 12(12), pages 1-22, June.
    15. Sheng, Chuang & Guo, Ziang & Lei, Jingzhi & Zhang, Shuyu & Zhang, Wenxuan & Chen, Weiming & Jiang, Xuefeng & Wang, Zhuo & Li, Xi, 2024. "Optimal energy management strategies for hybrid power systems considering Pt degradation," Applied Energy, Elsevier, vol. 360(C).

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