Discrimination and state-of-health diagnosis based on the discrete wavelet transform for a polymer electrolyte membrane fuel cell

This work investigates a new approach based on the discrete wavelet transform (DWT) that suitable for analyzing and evaluating output voltage signal (OVS) for discrimination method of a polymer electrolyte membrane fuel cell (PEMFC). Due to its ability to extract information from the non-stationary and transient phenomena simultaneously in both the time and frequency domain, the OVS is applied as source data in the DWT-based approach. By using the wavelet decomposition including the multi-resolution analysis (MRA) using the Daubechies wavelet (dB) as mother wavelet, the information on the electrochemical characteristics of a PEMFC can be extracted from a signal over a wide frequency range, thus the cells that have similar electrochemical characteristics can be eventually selected. In particular, the present study develops these investigations one step further by showing approximation An and detail Dn components extracted from variable PEMFC cells with different electrochemical characteristics. Experimental results show that DWT-based approach is clearly appropriate for the reliable SOH diagnosis of a PEMFC.