Aging characteristics and state-of-health estimation of retired batteries: an electrochemical impedance spectroscopy perspective

In this paper, the aging characteristics and state-of-health (SOH) estimation of retired batteries were studied by leveraging the electrochemical impedance spectroscopy (EIS) technique. A battery aging experiment was designed and implemented to monitor the aging process of batteries, after which a comprehensive analysis of the collected EIS data was conducted to characterize the corresponding aging properties of retired batteries. Based on the aging data analysis results, an equivalent circuit model (ECM) was constructed, and the correlation between ECM parameters and the battery age was identified. An EIS-based and ECM-based SOH estimation method for retired batteries was developed and demonstrated. Furthermore, to further leveraging the EIS data from battery aging tests, a Bayesian neural network-based SOH estimation method with automatic feature extraction was developed. Comparisons among the proposed model-based method, data-driven method, and state-of-the-art SOH estimation method for retired batteries were implemented. Overall, insights into the aging characteristics and SOH estimation of retired batteries were achieved by leveraging the EIS technique.