Lithium-ion Battery State-of-Health Estimation via Histogram Data, Principal Component Analysis, and Machine Learning

Abstract

Lithium-ion batteries are widely used in electric vehicle powertrain systems. As batteries age, their state of health (SOH), indicated by their usable capacity and power capability, decreases. For reliable battery operation, accurate estimation and prediction of SOH are essential. This paper proposes an algorithm for estimating battery capacity SOH from an open-source fast charging dataset with many different charge profile types. Histogram data is created from the measured time domain data and fed into a feedforward neural network (FNN). To capture the impact of different charge profiles on aging, current and state of charge (SOC) are multiplied together to create an additional synthetic input to the estimator. To reduce the number of inputs to the FNN to only those that contain valuable information, we use principal component analysis to reduce the total number of inputs by 80%. An SOH algorithm is proposed that can estimate capacity throughout the battery's life with a 1.03% root mean square percentage error (RMSPE) and 0.68% mean absolute percentage error (MAPE).