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Diagnostic method for PEM fuel cell states using probability Distribution-Based loss component analysis for voltage loss decomposition

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  • Shin, Donghoon
  • Yoo, Seungryeol

Abstract

This study proposed a novel method referred to as the loss component analysis (LCA) to represent the current state of fuel cells. The LCA method was derived from an independent component analysis (ICA) and used probability density functions of activation, ohmic, and concentration losses. This method determined three weights related to each loss component reflecting the fuel cell states, and the fuel cell conditions were diagnosed using deviations in weight from the reference weight at the normal state. The maximum increase in weight allocated to each loss component was found to have the most significant impact on changes in the state of the fuel cell from its normal state. Moreover, LCA was applied to both the data obtained from empirical models and the data acquired through experiments that mimic the three faults that could occur during fuel cell operation. The results were compared to demonstrate the validity of the proposed method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pb:s0306261922015975
    DOI: 10.1016/j.apenergy.2022.120340
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    References listed on IDEAS

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    1. 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.
    2. K. V. S. Bharath & Frede Blaabjerg & Ahteshamul Haque & Mohammed Ali Khan, 2020. "Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 13(12), pages 1-18, June.
    3. Subotić, Vanja & Menzler, Norbert H. & Lawlor, Vincent & Fang, Qingping & Pofahl, Stefan & Harter, Philipp & Schroettner, Hartmuth & Hochenauer, Christoph, 2020. "On the origin of degradation in fuel cells and its fast identification by applying unconventional online-monitoring tools," Applied Energy, Elsevier, vol. 277(C).
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