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This paper presents data-driven impedance-based state of health (SOH) estimation for commercial lithium-ion batteries across an SOH range of ~96% to ~60%. Battery health indicators at the transition frequency of the battery impedance Nyquist plot are utilized to develop an SOH estimator based on an artificial neural network (ANN). In addition, two more ANN-based SOH estimators utilizing some impedance magnitude and phase values are developed. Spearman correlation analysis is utilized to identify the frequency points at which the impedance magnitude and phase values show strong correlations with SOH values and are thus utilized as SOH indicators. The performance evaluation of the developed SOH estimators shows that the maximum root mean square error (RMSE) is equal to 1.39%, the maximum mean absolute error (MAE) is equal to 1.25%, the maximum mean absolute percentage error (MAPE) is equal to 1.55%, and the minimum coefficient of determination (R2) is equal to 0.983. 

This paper presents a state-of-health (SOH) estimation algorithm and hardware platform for ‎lithium-ion batteries. Based on features obtained from the battery’s electrochemical impedance ‎spectroscopy (EIS), an artificial neural network (ANN)-based SOH algorithm is developed. EIS measurements collected at different aging levels are utilized to train and test the SOH estimation algorithm. The minimum impedance magnitude and the impedance magnitude at zero phase show correlations with the battery SOH level and can be utilized to indicate the SOH value. The SOH estimation algorithm performance is evaluated, and the performance evaluation results indicate that the SOH estimation algorithm can be utilized to estimate the SOH. 

This paper presents characterization for equivalent circuit model (ECM) parameters variation for lithium-ion capacitor ‎‎ (LiC) under different voltage values. A set of experimentally obtained electrochemical impedance spectroscopy (EIS) data ‎for LiC is fitted using the simplex algorithm to obtain the values for ECM parameters. The model-‎fit EIS data is compared with the measured EIS data to validate the model.