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Fault Detection for PEM Fuel Cells via Analytical Redundancy: A Critical Review and Prospects

Author

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  • Mukhtar Sani

    (Université Grenoble Alpes, CEA, LITEN, F-38000 Grenoble, France)

  • Maxime Piffard

    (Université Grenoble Alpes, CEA, LITEN, F-38000 Grenoble, France)

  • Vincent Heiries

    (Université Grenoble Alpes, CEA, LETI, F-38000 Grenoble, France)

Abstract

Decarbonization of the transport sector could be achieved through fuel cell technology. The candidature of this technology is motivated by its high current density and lack of emissions. However, its widespread deployment is restrained by durability and reliability constraints. During normal operation, the fuel cell system supplies stable power to the load. Contrarily, when it is operated under faulty conditions, the system’s output power deteriorates, leading to low durability. It is therefore of paramount importance to ensure that the system is operated in a non-faulty condition. In this paper, we provide a critical review of the analytical-redundancy-based fault diagnosis methods for proton exchange membrane fuel cells (PEMFCs). An in-depth analysis of the various methods has been presented in terms of accuracy, complexity, implementability, and robustness to aging and dynamic operating conditions.

Suggested Citation

  • Mukhtar Sani & Maxime Piffard & Vincent Heiries, 2023. "Fault Detection for PEM Fuel Cells via Analytical Redundancy: A Critical Review and Prospects," Energies, MDPI, vol. 16(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5446-:d:1196353
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    References listed on IDEAS

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    1. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    2. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    3. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    4. Kai Song & Yu Lan & Xian Zhang & Jinhai Jiang & Chuanyu Sun & Guang Yang & Fengshuo Yang & Hao Lan, 2023. "A Review on Interoperability of Wireless Charging Systems for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-22, February.
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