Online implementation of SVM based fault diagnosis strategy for PEMFC systems

Abstract : In this paper, the topic of online diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems is addressed. In the diagnosis approach, individual cell voltages are used as the variables for diagnosis. The pattern classification tool Support Vector Machine (SVM) combined with designed diagnosis rule is used to achieve fault detection and isolation (FDI). A highly-compacted embedded system of the System in Package (SiP) type is designed and fabricated to monitor individual cell voltages and to perform the diagnosis algorithms. For validation, the diagnosis approach is implemented online on PEMFC experimental platform. Four concerned faults can be detected and isolated in real-time.
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Communication dans un congrès
6th International Conference on ”Fundamentals & Development of Fuel Cells” (FDFC), Feb 2015, Toulouse, France. 164, pp.284-293, 2016
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https://hal-cea.archives-ouvertes.fr/cea-01942197
Contributeur : Mathias Legrand <>
Soumis le : lundi 3 décembre 2018 - 08:22:29
Dernière modification le : mercredi 5 décembre 2018 - 01:22:19

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Paper_FDFC_zli_19012015-1.pdf
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  • HAL Id : cea-01942197, version 1

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Zhongliang Li, Rachid Outbib, Stefan Giurgea, Daniel Hissel, Samir Jemei, et al.. Online implementation of SVM based fault diagnosis strategy for PEMFC systems. 6th International Conference on ”Fundamentals & Development of Fuel Cells” (FDFC), Feb 2015, Toulouse, France. 164, pp.284-293, 2016. 〈cea-01942197〉

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