Correlation analysis and feature extraction using impedance spectroscopy over aging of lithium ion batteries

To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method. • Analysis of extensive EIS measurements over battery aging. • Impact analysis of the SoC on correlation between impedance and SoH. • Identification of the most suitable condition of a LIB to estimate the SoH. • Development of a feature extraction method for ML models based on EIS data. • Application of the correlation results in two ML models to estimate the SoH.