Rapid State of Health Assessment for Retired Lithium‐Ion Batteries Using Local‐Frequency Impedance Features

The rapid growth of electric vehicles has generated a large number of retired lithium‐ion batteries (LIBs), making efficient state of health (SOH) screening essential for large‐scale cascade utilization. However, conventional SOH evaluation methods rely on time‐consuming charge–discharge calibration, involve high testing costs, and are sensitive to batch inconsistencies, thereby limiting their large‐scale application. To address these issues, this study proposes a rapid and energy‐efficient SOH screening method for multi‐batch retired electric vehicle LIBs based on local‐frequency electrochemical impedance spectroscopy (EIS) features. First, EIS curves from multiple battery batches are analyzed to identify local‐frequency bands strongly correlated with SOH, improving feature robustness under heterogeneous aging conditions. Then, EIS data and equivalent circuit model (ECM) parameters are used to analyze feature‐SOH correlations, and an optimized regression model is developed to improve estimation accuracy. Finally, the EIS feature set is simplified to further enhance estimation efficiency. Results show that the proposed method reduces SOH estimation time to 2.5%–11.5% of that required by conventional capacity calibration, while maintaining the mean absolute error (MAE) within 2.6%. These findings demonstrate the practical potential of the proposed method for large‐scale retired battery screening.