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Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors

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  • Li, Zhongliang
  • Outbib, Rachid
  • Giurgea, Stefan
  • Hissel, Daniel
  • Giraud, Alain
  • Couderc, Pascal

Abstract

Reliability and durability are two key hurdles that prevent the widespread use of fuel cell technology. Fault diagnosis, especially online fault diagnosis, has been considered as one of the crucial techniques to break through these two bottlenecks. Although a large number of works dedicated fuel cell diagnosis have been published, the criteria of diagnosis, especially online diagnosis have not yet been clarified. In this study, we firstly propose the criteria used for evaluating a diagnosis strategy. Based on that, we experimentally demonstrate an online fault diagnosis strategy designed for Proton Exchange Membrane Fuel Cell (PEMFC) systems. The diagnosis approach is designed based on advanced feature extraction and pattern classification techniques, and realized by processing individual fuel cell voltage signals. We also develop a highly integrated electronic chip with multiplexing and high-speed computing capabilities to fulfill the precise measurement of multi-channel signals. Furthermore, we accomplish the diagnosis algorithm in real-time. The excellent performance in both diagnosis accuracy and speediness over multiple fuel cell systems is verified. The proposed strategy is promising to be utilized in various fuel cell systems and promote the commercialization of fuel cell technology.

Suggested Citation

  • Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Giraud, Alain & Couderc, Pascal, 2019. "Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors," Renewable Energy, Elsevier, vol. 135(C), pages 1435-1444.
  • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:1435-1444
    DOI: 10.1016/j.renene.2018.09.077
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    References listed on IDEAS

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    1. Wang, Junye, 2015. "Barriers of scaling-up fuel cells: Cost, durability and reliability," Energy, Elsevier, vol. 80(C), pages 509-521.
    2. Laetitia Dubau & Luis Castanheira & Frédéric Maillard & Marian Chatenet & Olivier Lottin & Gaël Maranzana & Jérôme Dillet & Adrien Lamibrac & Jean‐Christophe Perrin & Eddy Moukheiber & Assma ElKaddour, 2014. "A review of PEM fuel cell durability: materials degradation, local heterogeneities of aging and possible mitigation strategies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 540-560, November.
    3. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Li, Yongdong, 2015. "Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space," Applied Energy, Elsevier, vol. 148(C), pages 260-272.
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    Cited by:

    1. Kalina, Jacek & Świerzewski, Mateusz, 2019. "Identification of ORC unit operation in biomass-fired cogeneration system," Renewable Energy, Elsevier, vol. 142(C), pages 400-414.
    2. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    3. Akimoto, Yutaro & Okajima, Keiichi, 2021. "Simple on-board fault-detection method for proton exchange membrane fuel cell stacks using by semi-empirical curve fitting," Applied Energy, Elsevier, vol. 303(C).
    4. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
    5. 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).
    6. Wang, Hanqing & Gaillard, Arnaud & Hissel, Daniel, 2019. "A review of DC/DC converter-based electrochemical impedance spectroscopy for fuel cell electric vehicles," Renewable Energy, Elsevier, vol. 141(C), pages 124-138.

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