Active Cell Balancing Approach for Efficient Battery Management System

Abstract

This research enhances the operational efficiency of electric vehicle (EV) energy storage by transitioning from conventional passive equalization to an active cell-balancing framework. Unlike passive methods that dissipate surplus energy as thermal waste, the suggested configuration dynamically reroutes power among cells to minimize energy loss. An Extended Kalman Filter (EKF) algorithm is applied to precisely calculate the State of Charge (SOC) of lithium-ion cells while accommodating system noise and modeling inaccuracies. During simulation, the EKF framework registered an SOC estimation root mean square error (RMSE) of 3.24% alongside a stabilization period of approximately 30 s . When subjected to a standard 5% initial SOC discrepancy, the active balancing topology synchronized the cells within 34 s  during standard operation, 11.6 s  during charging phases, and 53 s under discharge loads, achieving a peak balancing efficiency of 97.05%. For a more extreme 40% initial mismatch, the equalization window expanded to 241 s  with a recorded efficiency of 83.33%. Ultimately, these findings confirm that fusing EKF-driven SOC tracking with buck-boost active equalization significantly bolsters energy retention and cell uniformity within battery management systems.