Ultrafast Feature Extraction for Lithium-Ion Battery Health Assessment

Machine learning (ML) becomes an important technology in battery health assessment. The mapping from feature usually extracted from charging voltage or temperature to unmeasurable state of health (SOH) can be found by training a ML-based SOH estimator. However, the feature may become invalid when operation conditions change or be inaccessible from incomplete charging. For tackling these challenges, various entropies are investigated thoughtfully. Afterwards, spectral entropy and its variants, i.e., composite multi-scale entropy and hierarchical entropy are screened out. Ultrafast SOH feature extraction is therefore achieved where only 2 seconds of voltage data is needed. Finally, the effectiveness of the proposed method is verified by using the accelerated aging dataset from NMC batteries.