SOH Estimation Algorithm and Hardware Platform for Lithium-ion Batteries

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.