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Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review

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  • Liu, Hao
  • Chen, Jian
  • Hissel, Daniel
  • Lu, Jianguo
  • Hou, Ming
  • Shao, Zhigang

Abstract

Prognostics is a promising solution to the short lifetime and high-cost bottlenecks of proton exchange membrane fuel cells (PEMFCs). The advances of PEMFCs prognostics research can be divided into two main categories, namely, the innovations of prognostics methods and the innovations of degradation indexes. Among them, the degradation indexes are the prerequisites for accurate prognostics of PEMFCs. Although many prognostics methods have been proposed in recent years to improve the performance of prognostics, the research on degradation indexes of PEMFCs has been slow, and few studies have focused on more effective and more general degradation indexes. Currently, the most widely used degradation indexes are still the traditional indexes, which only reflect the macro-scale degradation of PEMFCs, such as voltage and power. In addition, the traditional indexes change as operating conditions change. For PEMFCs under dynamic operating conditions, especially in transportation applications, it is difficult to obtain accurate degradation information based on the traditional indexes. Due to the lack of more accurate, comprehensive, and general degradation indexes, the existing prognostics methods inevitably have limitations and deficiencies. The purpose of this paper is to provide a full review of the latest research advances in the prognostics of PEMFCs, especially the degradation indexes. Moreover, the challenges and future directions of prognostics research are also analyzed.

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  • Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
  • Handle: RePEc:eee:rensus:v:123:y:2020:i:c:s1364032120300186
    DOI: 10.1016/j.rser.2020.109721
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    16. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2021. "Prognosis of fuel cell degradation under different applications using wavelet analysis and nonlinear autoregressive exogenous neural network," Renewable Energy, Elsevier, vol. 179(C), pages 802-814.
    17. Deng, Shutong & Zhang, Jun & Zhang, Caizhi & Luo, Mengzhu & Ni, Meng & Li, Yu & Zeng, Tao, 2022. "Prediction and optimization of gas distribution quality for high-temperature PEMFC based on data-driven surrogate model," Applied Energy, Elsevier, vol. 327(C).
    18. 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.
    19. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    20. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    21. Chen, Hong & Zhan, Zhigang & Jiang, Panxing & Sun, Yahao & Liao, Liwen & Wan, Xiongbiao & Du, Qing & Chen, Xiaosong & Song, Hao & Zhu, Ruijie & Shu, Zhanhong & Li, Shang & Pan, Mu, 2022. "Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA," Applied Energy, Elsevier, vol. 310(C).
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    23. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    24. 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).

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