Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory

Abstract

Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference ΔV in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.