Performance Classification and Remaining Useful Life Prediction of Lithium Batteries Using Machine Learning and Early Cycle Electrochemical Impedance Spectroscopy Measurements

Abstract: We presents an approach for early cycle classification of lithium-ion batteries into high and low-performing categories, coupled with the prediction of their remaining useful life (RUL) using a linear lasso technique. Traditional methods often rely on extensive cycling and the measurement of a large number of electrochemical impedance spectroscopy (EIS) frequencies to assess battery performance, which can be time and resource consuming. In this study, we propose a methodology that leverages specific EIS frequencies to achieve accurate classification and RUL prediction within the first few cycles of battery operation. Notably, given only the 20 kHz impedance response, our support vector machine (SVM) model classifies batteries with 100\% accuracy. Additionally, our findings reveal that battery performance classification is frequency agnostic within the high frequency ($<20$ kHz) to low-frequency (32 mHz) range. Our model also demonstrates accurate RUL predictions with $R^2>0.96$ based on the out of phase impedance response at a single high (20 kHz) and a single mid-frequency (8.8 Hz), in conjunction with temperature data. This research underscores the significance of the mid-frequency impedance response as merely one among several crucial features in determining battery performance, thereby broadening the understanding of factors influencing battery behavior.