State of health estimation for lithium-ion batteries based on multi-scale frequency feature and time domain feature fusion method

Abstract Accurately estimating the state of health (SOH) of lithium-ion batteries is important for improving battery safety performance. The single time-domain feature extraction is hard to efficiently extract discriminative features from strongly nonlinear coupled data, leading to difficulties in accurately estimating the battery SOH. To this end, this paper proposes a multi-scale frequency domain feature and time-domain feature fusion method for SOH estimation of lithium-ion batteries based on the transformer model. First, the voltage, current, temperature, and time information of the battery are extracted as time-domain features; second, the battery signal is processed by a multi-scale filter bank based on Mel-frequency cepstral coefficients (MFCCs) to obtain the multi-scale frequency-domain features; then, a parallel focusing network (PFN) is designed to fuze the time-domain features with the frequency-domain features, which yields low-coupling complementary discriminative features; finally, constructing the SOH estimation mechanism based on the transformer deep network model. The algorithm is validated by NASA and Oxford datasets, and the mean absolute error (MAE) and root-mean-square error (RMSE) are as low as 0.06% and 0.23%, respectively.