Discrete wavelet transform-based characteristic analysis and SOH diagnosis for a Li-Ion cell

This paper introduces the characteristic analysis and state-of-health (SOH) diagnosis for a Li-Ion cell based on discrete wavelet transform (DWT). The DWT is a powerful tool in the analysis of the discharging/charging voltage signal (DCVS) of a Li-Ion cell with non-stationary and transient phenomena. DWT-based multi-resolution analysis (MRA) is applied for extracting the information on the electrochemical characteristic in both time and frequency domain simultaneously. Wavelet decomposition based on the selection of the order 3 Daubechies wavelet (dB3) and scale 5 as the best wavelet function and the optimal decomposition scale are implemented. In particular, this present study develops these investigations one step further by showing high/low frequency components extracted from variable Li-Ion cells with different electrochemical characteristics caused by aging effect. The experimental results show the clearness of the DWT-based approach for the reliable diagnosis of the SOH.