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Journal Articles Applied Energy Year : 2016

Online implementation of SVM based fault diagnosis strategy for PEMFC systems

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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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Dates and versions

cea-01846859 , version 1 (07-12-2018)

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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. Applied Energy, 2016, 164, pp.284-293. ⟨10.1016/j.apenergy.2015.11.060⟩. ⟨cea-01846859⟩
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