State-of-health estimation of lithium-ion batteries using multiple correlation analysis-based feature screening and optimizing echo state networks with the weighted mean of vectors

Lithium-ion batteries (LIBs) are widely employed in electric vehicles (EVs) and energy storage systems owing to their high energy density, low self-discharge rate, and superior longevity. As a vital evaluation index for the degradation state of LIBs, the state of health (SOH) must be accurately estimated. This study adopts an electrochemical impedance spectroscopy (EIS)-based equivalent circuit model (ECM) to estimate battery SOH. The internal dynamic electrical behaviors of LIBs are decoupled by the distribution of relaxation times (DRT) method, whereby the critical degradation features are extracted from the DRT curves and the fitted ECM parameters. Subsequently, referring to the ensemble learning approach (ELA), the multiple correlation analysis (MCA) is utilized to identify the most pertinent degradation features. The battery data are subjected to a comprehensive analysis with five types of correlations to ascertain the optimal number and combination of health indicators (HIs). Furthermore, the weighted mean of vectors (INFO) algorithm is employed to optimize the hyper- parameters in the echo state network (ESN) model for SOH estimation. The echo state networks model optimized with the weighted mean of vectors algorithm (INFO-ESN) is verified with eight types of LIBs under different operating conditions. Experimental results of the proposed method manifest that the root mean square error (RMSE) and mean absolute error (MAE) of the battery SOH range from 0.32 % to 1.06 %, thereby verifying the accuracy and robustness of the proposed method.