Model-Based SOH Estimation of Battery Systems using EKF in Simulink for Smart Energy Applications

Abstract

Correctly estimating the State of Health (SOH) of batteries is an important consideration in order to ensure reliable and efficient battery management system (BMS) operations, particularly in smart energy related applications. This paper describes a model-based SOH estimation of lithium-ion battery systems using the Extended Kalman Filter (EKF) simulation using Simulink. The proposed method made use of real-time battery measurements such as voltage, current, and State of Charge (SoC) to estimate the degradation of battery capacity as it aged. A dynamic battery model is constructed to simulate realistic charging and discharging conditions, and the EKF algorithm is integrated to track the SOH under varying load profiles. Simulation results demonstrate that the EKF provides robust and accurate SOH estimation, with minimal deviation from the true degradation profile. This work contributes to the development of intelligent BMS capable of adaptive control, fault detection, and lifespan prediction in modern energy storage systems.