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Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model

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  • Pan, Rui
  • Yang, Duo
  • Wang, Yujie
  • Chen, Zonghai

Abstract

The durability of proton exchange membrane fuel cell is worse than the traditional power generation system, which restricts its commercial applications. Accurate state of health of fuel cell plays an important role in ensuring its long-life operation and minimizing maintenance costs. This paper focuses on health forecasting based on electrochemical impedance and analytical equivalent circuit model. The proposed model matches the Nyquist diagram by electrode dynamics analysis, and then the complex nonlinear least square method is used to identify the model parameters. In order to describe the degradation accurately, the parameters with significant aging properties are selected to estimate the state of health based on linear regression. Then, the estimated impedance of four characteristic frequency points, which can represent the overall outline of the impedance spectrum, is used to evaluate the accuracy of the proposed method. The effectiveness of aging datasets are verified by Kramers-Kronig transformation, and the predictive capability of the proposed method are demonstrated by two aging datasets. Compared with other method, the experimental results show the superiority of the proposed method, which can provide accurate health forecasting and help to improve performance of the voltage degradation prediction.

Suggested Citation

  • Pan, Rui & Yang, Duo & Wang, Yujie & Chen, Zonghai, 2020. "Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220312925
    DOI: 10.1016/j.energy.2020.118185
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    References listed on IDEAS

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    1. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
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    Citations

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    Cited by:

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    3. Huang, Ruike & Peng, Yiqiang & Yang, Jibin & Xu, Xiaohui & Deng, Pengyi, 2022. "Correlation analysis and prediction of PEM fuel cell voltage during start-stop operation based on real-world driving data," Energy, Elsevier, vol. 260(C).
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    5. Huang, Weifeng & Niu, Tong & Zhang, Caizhi & Fu, Zuhang & Zhang, Yuqi & Zhou, Weijiang & Pan, Zehua & Zhang, Kaiqing, 2023. "Experimental study of the performance degradation of proton exchange membrane fuel cell based on a multi-module stack under selected load profiles by clustering algorithm," Energy, Elsevier, vol. 270(C).
    6. Lee, Jiseung & Salihi, Hassan & Lee, Jaeseung & Ju, Hyunchul, 2022. "Impedance modeling for polymer electrolyte membrane fuel cells by combining the transient two-phase fuel cell and equivalent electric circuit models," Energy, Elsevier, vol. 239(PC).
    7. 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).
    8. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    9. Deng, Huiwen & Hu, Weihao & Cao, Di & Chen, Weirong & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2022. "Degradation trajectories prognosis for PEM fuel cell systems based on Gaussian process regression," Energy, Elsevier, vol. 244(PA).
    10. Zhao, Lei & Yuan, Hao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Tang, Wei & Ming, Pingwen & Dai, Haifeng, 2023. "Inconsistency evaluation of vehicle-oriented fuel cell stacks based on electrochemical impedance under dynamic operating conditions," Energy, Elsevier, vol. 265(C).
    11. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).

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