A Distribution of Relaxation Time Approach on Equivalent Circuit Model Parameterization to Analyse Li-ion Battery Degradation

Li-ion batteries' complex and dynamic electrochemical behavior pose a significant challenge for their diagnostics and prognostics. Electrochemical Impedance Spectroscopy (EIS) is a valuable measurement technique that provides crucial insight into battery behavior. Transforming the EIS impedance data across various frequencies into equivalent electrical circuit elements tailored to the battery's physical and chemical characteristics is valuable for interpreting this data. Distribution of Relaxation Times (DRT) analysis is such an approach and results in a distribution of time constants that effectively characterize the RC networks of the battery's equivalent circuit model (ECM). Modeling the battery with detailed consideration of EIS data and minimizing the information that is lost in this modeling stage is the crucial advantage of the DRT technique. In this work, we present a fully automated procedure for modeling a substantial volume of EIS data using DRT. This involves the automatic selection of prominent peaks that persist throughout the entire battery aging process, effectively enhancing computational efficiency. It furthermore includes data pre-processing steps and frequency range selection, making DRT analysis more efficient and robust by employing the least squares fitting (LSQF) algorithm. The findings in applying our procedure to published EIS spectra of Li-ion battery cells indicate that the presented approach yields a high level of precision in tracking the ECM parameters to analyse the degradation of these cells. Further proof of the quality of our procedure lies in the consistent low dispersion of the various fits, which remain consistent across the entire dataset.