Online implementation of SVM based fault diagnosis strategy for PEMFC systems - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Journal Articles Applied Energy Year : 2016

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.
Fichier principal
Vignette du fichier
Li2016.pdf (1.65 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

Cite

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⟩
385 View
349 Download

Altmetric

Share

Gmail Facebook X LinkedIn More