Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions.@en

Accurately predicting the battery's aging trajectory is required to ensure the safe and reliable operation of electric vehicles (EVs) and is also the fundamental technique toward 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 multistep charging process in the state of health (SOH) assessment. First, 12 direct features are extracted from the voltage curve, and the degradation mechanisms strongly correlated to these features are analyzed in detail. Integrating the degradation mechanism and correlation analysis, a data feature construction strategy is designed to categorize 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 generalization 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 generalization and feasibility in SOH estimation, and 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.

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