Implementation of discrete wavelet transform-based discrimination and state-of-health diagnosis for a polymer electrolyte membrane fuel cell

This research investigates a new approach based on the discrete wavelet transform (DWT) that suitable for analyzing and evaluating output terminal voltage signal (OTVS) for discrimination analysis of a polymer electrolyte membrane fuel cell (PEMFC). Due to its ability for extracting information from the non-stationary and transient phenomena simultaneously in both time and frequency domain, the OTVS can be 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 the OTVS over a wide frequency range. Thus, the cells that have similar electrochemical characteristics can be eventually discriminated. In particular, this present research develops these investigations one step further by showing low-frequency components (approximation An) and high-frequency components (detail Dn) extracted from variable single cells with different electrochemical characteristics. Experimental results show that DWT-based approach is clearly appropriate for the reliable SOH diagnosis for a PEMFC.