Integrating Machine Learning with Advanced Features of L9963E BMS for Enhanced State of Charge Estimation

This paper presents a novel methodology for enhanced state-of-charge (SOC) estimation in lithium-ion batteries, integrating Support Vector Regression (SVR) with Hybrid Pulse Power Characterization (HPPC) experimental data and leveraging the advanced diagnostic and monitoring features of the L9963E battery management system (BMS). The proposed approach combines comprehensive hardware characterization including cell sensing, balancing, adaptive charge control, fault detection, and real-time monitoring with data-driven algorithmic techniques validated under various C-rates and temperature conditions. In this study, lithium-ion cells were subjected to extensive charge and discharge cycles, with datasets acquired through the L9963E BMS and analyzed using SVR models. Experimental results demonstrate that the SVR model achieves a Root Mean Square Error (RMSE) of 0.72%, Mean Absolute Error (MAE) of 0.55%, and a coefficient of determination (R²) of 0.983 for SOC estimation, outperforming conventional methods such as coulomb counting, static lookup tables, and linear regression models. The integration of SVR with HPPC-derived datasets enables adaptive and precise SOC prediction, overcoming limitations in accuracy and robustness commonly encountered in battery management. To our knowledge, this is the first study to combine SVR with HPPC data using the L9963E BMS platform for real-time SOC estimation. The findings highlight the potential of machine learning-enabled BMS solutions to deliver more reliable, adaptive, and robust battery management for electric vehicle and energy storage applications, significantly improving safety, efficiency, and battery longevity. This work provides valuable insights into the operational intricacies and future directions for intelligent battery management systems.