State of health forecasting of Lithium-ion batteries applicable in real-world operational conditions

Currently, several methods for battery state of health (SOH) prediction exist which are applicable to battery electric vehicles (BEV). However, only few research has been conducted on SOH forecasting based on features that encode causes for battery ageing applicable in real world applications. This paper proposes a machine learning method for SOH forecasting applicable for BEV fleet managers and battery designers in real world applications. As model inputs, we use the battery's operation time within certain operation ranges defined by combinations of the battery signals current, state of charge (SOC) and temperature. Different variants of this temporal aggregation of the battery operation time and of the operation ranges of the battery signals are examined. Our findings state that combining different cycle window widths ww to one training data set improves the generalization of the model. Also, we find that the fineness of the operational ranges of the signals does not limit the model's performance if ww is larger than 100 cycles or different ww are combined.