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Online diagnosis of PEMFC by analyzing individual cell voltages

Abstract : Polymer Electrolyte Membrane Fuel Cell (PEMFC) is a promising power source for a wide range of applications. Fault diagnosis, especially online fault diagnosis, is an essential issue to promote the development and widespread use of PEMFC technology. This paper proposes a diagnosis approach for large PEMFC stack. In this approach, flooding fault is concerned, individual cell voltages are chosen as original variables for diagnosis. A dimension reduction method Fisher linear discrimination (FDA) is adopted to extract the features from the cell voltage composed vectors, after that, a classification methodology Gaussian mixture model (GMM) is applied for fault detection. Flooding experiments were conducted on a 20-cell stack to test the approach, the obtained results showed that data points can be classified to different states of health with a high accuracy. It is also verified that the real-time implementation of the algorithm is feasible.
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https://hal.archives-ouvertes.fr/hal-02476449
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Submitted on : Wednesday, February 12, 2020 - 4:41:59 PM
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Zhongliang Li, Rachid Outbib, Daniel Hissel, Stefan Giurgea. Online diagnosis of PEMFC by analyzing individual cell voltages. 2013 European Control Conference (ECC), Jul 2013, Zurich, France. pp.2439-2444, ⟨10.23919/ECC.2013.6669725⟩. ⟨hal-02476449⟩

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