Multi-step Fast Charging based State of Health Estimation of Lithium-ion Batteries

Accurately predicting the battery’s ageing trajectory is required to ensure the safe and reliable operation of electric vehicles (EVs), and is also the fundamental technique towards residual value assessment. As a critical enabler for mainstreaming EVs, fast charging has presented formidable challenges to health prognosis technology. This study systematically compares the performance of features extracted from the multi-step charging process in the state of health (SOH) assessment. First, twelve direct features are extracted from the voltage curve, and the degradation mechanisms strongly correlated to these features are analysed in detail. Integrating the degradation mechanism and correlation analysis, a data feature construction strategy is designed to categorise extracted features into groups. Then, the performance of different features extracted from the fast charging process in the SOH assessment is compared regarding estimation accuracy. Finally, the generalisation and feasibility of the optimal data feature are verified with different fast charging protocols and training data sizes. The verification results indicate that the data feature representing fused degradation modes has excellent generalisation and feasibility in SOH estimation, the mean absolute error (MAE) and root-mean-squared error (RMSE) for various cells under different decline patterns are within 0.90% and 1.10%, respectively.