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Predictive Maintenance of Proton-Exchange-Membrane Fuel Cells for Transportation Applications

Author

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  • Gaultier Gibey

    (Université Marie et Louis Pasteur, UTBM, SupMicroTech-ENSMM, CNRS, Institut FEMTO-ST, FCLAB, 90000 Belfort, France)

  • Elodie Pahon

    (Université Marie et Louis Pasteur, UTBM, SupMicroTech-ENSMM, CNRS, Institut FEMTO-ST, FCLAB, 90000 Belfort, France)

  • Noureddine Zerhouni

    (Université Marie et Louis Pasteur, UTBM, SupMicroTech-ENSMM, CNRS, Institut FEMTO-ST, FCLAB, 90000 Belfort, France)

  • Daniel Hissel

    (Université Marie et Louis Pasteur, UTBM, SupMicroTech-ENSMM, CNRS, Institut FEMTO-ST, FCLAB, 90000 Belfort, France
    Institut Universitaire de France (IUF), 103 Boulevard Saint-Michel, 75005 Paris, France)

Abstract

Proton-Exchange-Membrane Fuel Cell (PEMFC) systems are proving to be a promising solution for decarbonizing various means of transport, especially heavy ones. However, their reliability, availability, performance, durability, safety and operating costs are not yet fully competitive with industrial and commercial systems (actual systems). Predictive maintenance (PrM) is proving to be one of the most promising solutions for improving these critical points. In this paper, several PrM approaches will be developed considering the constraints of actual systems. The first approach involves estimating the overall State of Health (SOH) of a PEMFC operating under a dynamic load according to an FC-DLC (Fuel Cell Dynamic Load Cycle) profile, using a Health Indicator (HI). This section will also discuss the relevance of current End-of-Life (EoL) indicators by putting the performance, safety and economic profitability of PEMFC systems into perspective. The second approach involves predicting the voltage of the PEMFC while operating under this same profile in order to estimate its overall Remaining Useful Life (RUL). Finally, the last approach proposed will make it possible to estimate the time when it will be worthwhile, or even economically necessary, to replace a degraded PEMFC with a new one.

Suggested Citation

  • Gaultier Gibey & Elodie Pahon & Noureddine Zerhouni & Daniel Hissel, 2025. "Predictive Maintenance of Proton-Exchange-Membrane Fuel Cells for Transportation Applications," Energies, MDPI, vol. 18(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2957-:d:1671680
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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. Viviana Cigolotti & Matteo Genovese & Petronilla Fragiacomo, 2021. "Comprehensive Review on Fuel Cell Technology for Stationary Applications as Sustainable and Efficient Poly-Generation Energy Systems," Energies, MDPI, vol. 14(16), pages 1-28, August.
    3. Eliseo Curcio, 2025. "Techno-Economic Analysis of Hydrogen Production: Costs, Policies, and Scalability in the Transition to Net-Zero," Papers 2502.12211, arXiv.org.
    4. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    5. Liu, Zhao & Chen, Huicui & Zhang, Tong, 2022. "Review on system mitigation strategies for start-stop degradation of automotive proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 327(C).
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