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A signal-based method for fast PEMFC diagnosis

Author

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  • Pahon, E.
  • Yousfi Steiner, N.
  • Jemei, S.
  • Hissel, D.
  • Moçoteguy, P.

Abstract

This paper deals with a novel signal-based method for fault diagnosis of a proton exchange membrane fuel cell (PEMFC). Thanks to an in-lab test bench used for the experimental tests, various parameters can be recorded as electrical or fluidic measurements. The chosen input signal for the diagnosis uses no additional expensive and no intrusive sensors specifically dedicated for the diagnosis task. It uses insofar only the already existing sensors on the system. This paper focuses on the detection and identification of a high air stoichiometry (HAS) fault. The wavelet transform (WT) and more precisely the energy contained in each detail of the wavelet decomposition is used to diagnose quickly an oversupply of air to the fuel cell system. Finally, some experimental results are presented according to different input signals, in order to prove the efficiency of the patented method.

Suggested Citation

  • Pahon, E. & Yousfi Steiner, N. & Jemei, S. & Hissel, D. & Moçoteguy, P., 2016. "A signal-based method for fast PEMFC diagnosis," Applied Energy, Elsevier, vol. 165(C), pages 748-758.
    • Handle: RePEc:eee:appene:v:165:y:2016:i:c:p:748-758
    DOI: 10.1016/j.apenergy.2015.12.084
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    3. Oh, Hwanyeong & Lee, Won-Yong & Won, Jinyeon & Kim, Minjin & Choi, Yoon-Young & Han, Soo-Bin, 2020. "Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 277(C).
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    6. Logan Battrell & Aubree Trunkle & Erica Eggleton & Lifeng Zhang & Ryan Anderson, 2017. "Quantifying Cathode Water Transport via Anode Relative Humidity Measurements in a Polymer Electrolyte Membrane Fuel Cell," Energies, MDPI, vol. 10(8), pages 1-16, August.
    7. Feng Han & Ying Tian & Qiang Zou & Xin Zhang, 2020. "Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System," Energies, MDPI, vol. 13(10), pages 1-18, May.
    8. Shin, Donghoon & Yoo, Seungryeol, 2023. "Diagnostic method for PEM fuel cell states using probability Distribution-Based loss component analysis for voltage loss decomposition," Applied Energy, Elsevier, vol. 330(PB).
    9. Chen, Huicui & Zhao, Xin & Qu, Bingwang & Zhang, Tong & Pei, Pucheng & Li, Congxin, 2018. "An evaluation method of gas distribution quality in dynamic process of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 232(C), pages 26-35.
    10. Yu Zang & Wei Shangguan & Baigen Cai & Huashen Wang & Michael G Pecht, 2019. "Methods for fault diagnosis of high-speed railways: A review," Journal of Risk and Reliability, , vol. 233(5), pages 908-922, October.
    11. Baricci, Andrea & Mereu, Riccardo & Messaggi, Mirko & Zago, Matteo & Inzoli, Fabio & Casalegno, Andrea, 2017. "Application of computational fluid dynamics to the analysis of geometrical features in PEM fuel cells flow fields with the aid of impedance spectroscopy," Applied Energy, Elsevier, vol. 205(C), pages 670-682.
    12. Pedro Andrade & Khaled Laadjal & Adérito Neto Alcaso & Antonio J. Marques Cardoso, 2024. "A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues," Energies, MDPI, vol. 17(3), pages 1-45, January.
    13. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    14. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    15. 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).
    16. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.

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