Electrochemical Impedance Spectroscopy (EIS) and Machine Learning based Battery State of Health (SoH) Estimation

While Li-ion batteries have proven long lifetimes, an accurate assessment of the battery ca-pacity and its remaining life cannot yet be made using current Battery Management Systems (BMS) devices. Battery sensors used in BMS typically mon-itor voltage, current and temperature of the battery, in order to predict the state of health (SoH) of the battery. SoH is a measure that indicates the remaining capacity that had been affected by degradation. Information obtained by monitoring voltage, current and temperature are often not sufficient to predict SoH. In this study we captured extra information from interfacial layers of the battery through applying Electrochemical Impedance Spectroscopy (EIS) and employed a XGBoost-based machine learning approach to train our models. The results show that SoH of batteries can be predicted with 90% accuracy, 95% confidence and 82% reliability. Additionally, it was shown that accuracy could be maintained with little to no change even when the number of features was dramatically reduced and the sample size was minimal, thus making this method very practical for embedded EIS/AI based solutions.