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 A
_n
and detail D
_n
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.