This work investigates a pattern recognition-based diagnosis approach as an application of the Hamming neural network to the identification of suitable fuel cell model parameters, which aim to diagnose state-of health (SOH) for a polymer electrolyte membrane (PEM) fuel cell. The fuel cell output voltage (FCOV) patterns of the 20 PEM fuel cells were measured, together with the model parameters, as representative patterns. Through statistical analysis of the FCOV patterns for 20 single cells, the Hamming neural network is applied for identification of the representative FCOV pattern that matches most closely of the pattern of the arbitrary cell to be measured. Considering, the selected cell's ΔRd is properly applied to diagnose SOH of an arbitrary cell through the comparison with those of fully fresh and aged cells with the minimum and maximum of the ΔRd in experimental cell group, respectively.
Pattern recognition-based state-of-health prediction for a PEM fuel cell
EVS, Electric Vehicle Symposium, 26 ; 1496-1503
2012
8 Seiten, 12 Bilder, 2 Tabellen, 10 Quellen
Conference paper
English
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