Abstract
The inconsistent charge and discharge patterns of electric vehicle batteries, coupled with their operation across varying voltage and current levels, pose a challenge for accurate capacity and state of health (SOH) assessment. Traditional methods rely on regular calibration, requiring controlled charge and discharge cycles, which are impractical in real-world scenarios. This research demonstrates an analysis-based method to obtain labeled capacity and SOH values in such conditions. This method not only provides labeled SOH values but also extracts health features that can be used for data-driven prediction of capacity or SOH.•Incremental capacity analysis (ICA) method has been presented to be used with electric vehicle (EV) battery data.•The approach to extract health features from a EV battery using ICA method as a function of age of the battery has been presented which can be used along with a machine learning or deep learning model.•State of health has been calculated for a vehicle battery using the proposed method.