Smart Health Evaluation for Lithium‐Ion Battery With Super‐Short‐Segment Charging

Abstract Accurate state of health estimation is crucial for the reliable operation of lithium‐ion batteries in electric vehicles. The charging curve contains valuable features for health evaluation, but real‐world charging often lacks sufficient data due to the users’ early recharging habits. A smart method is proposed for accurate battery health estimation using super‐short charging segments. This method combines a degradation mechanism‐guided Scale‐Invariant Feature Transform for smart health feature identification with machine learning for health evaluation. Validation with 87 batteries with various chemistries, formats, and capacities from 6 manufacturers demonstrates its efficacy. Regardless of battery specifications, health features can be identified automatically from the charging data. The method promises high accuracy (estimation error as low as 1.97%) even with super‐short charging covering 10% state of charge span, where all the existing health feature extraction approaches fail. This method provides new avenues for battery health evaluation in uncertain real‐world electric vehicle applications.