This study extends electrochemical impedance spectroscopy (EIS) to battery module level to detect state of charge (SOC) and state of health (SOH) imbalances among series-connected cells. By comparing single-point diagnostics with Pearson correlation and support vector machine (SVM) classification, the results demonstrate the potential of advanced analysis techniques for identifying internal imbalances in battery energy storage systems (BESS).

Effective battery management systems are essential for battery energy storage systems (BESSs), particularly for managing inhomogeneities among series-connected cells requiring sophisticated diagnostic methods. Although electrochemical impedance spectroscopy (EIS) is a recognized diagnostic tool, its application has been mainly limited to individual cells. This limitation restricts the ability to diagnose the performance of entire battery modules. By extending application of EIS to the module level, this study provides a more comprehensive perspective on battery diagnostics. Reflecting real-world challenges for battery modules, it focuses on modules with series-connected cells under varying conditions. It encompasses two scenarios: state of charge (SOC) and state of health (SOH) imbalances. The module-level EIS measurements are analyzed by extending the commonly used single-point impedance diagnostic with two novel approaches: Pearson correlation analysis and support vector machine (SVM) classification. The findings show that while single-point impedance diagnostic has limitations in complex sample pools, Pearson correlation analysis and SVM classification are more effective methods that provide promising results in detecting and understanding cell imbalances. This study provides insights into the impact of cell SOC and SOH imbalances on the module's EIS, and it demonstrates the potential of advanced analytical techniques to improve the diagnostics of battery modules in BESSs.