Online implementation of SVM based fault diagnosis strategy for PEMFC systems

Zhongliang Li (1, 2, 3) , Rachid Outbib (4) , Stefan Giurgea (1, 2) , Daniel Hissel (1, 2) , Samir Jemei (2, 1) , Alain Giraud (5) , Sébastien Rosini (6)

1 FCLAB - Fuel Cell LAB : Vers des Systèmes Pile à Combustible Efficients
2 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
3 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
4 LSIS - Laboratoire des Sciences de l'Information et des Systèmes
5 DACLE-LIST - Département d'Architectures, Conception et Logiciels Embarqués-LIST
6 LITEN - Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux

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.

Mots clés: PEMFC system; System in Package; Fault diagnosis; SVM classification; Online implementation

Domaines: Sciences de l'ingénieur [physics]; Mécanique des fluides [physics.class-ph]; Thermique [physics.class-ph]; Automatique / Robotique

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⟩