State of Charge (SOC) Estimation for a Battery Management Syatem on Microgrid Using Extended Kalman Filter

Abstract

Power storage systems and electric vehicles rely on precise State of Charge (SoC) assessment for their BMS. Due to the non-linear nature of lithium-ion batteries, traditional approaches such as Coulomb counting and OCV frequently fail under dynamic loads. In order to give precise, real-time SoC estimation, this work utilizes an Extended Kalman Filter (EKF) in MATLAB/Simulink with an equivalent circuit model. Even when dealing with non-linearities, EKF converges to accurate, noise-immune results efficiently. Its minimal processing overhead makes it suitable for complicated BMS, opens the way for future SOH estimates and HIL integration, and makes it possible to implement in real-time embedded systems.