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

SUBMITTER: Li Q 

PROVIDER: S-EPMC12463020 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature