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

• The DWT is useful for analyzing signal with non-stationary and transient phenomena.
• The output terminal voltage signal (OTVS) of a PEMFC is used as original signal.
• The DWT-based multi-resolution analysis (MRA) with Daubechies dB3 wavelet is used.
• An outstanding discrimination result among variable single cells is provided.
• The proposed approach shows the clearness for the reliable SOH diagnosis of a PEMFC.

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.