State of Health (SOH) Estimation of Simscape Table-Based Lithium-Ion Battery for Microgrid Using Extended Kalman Filter

Abstract

The longevity, safety, and dependability of Li-ion batteries used in EVs and energy storage systems depend on precise SOH estimation. Stable energy supply, integration of renewable energy sources, and system resiliency in microgrid topologies are all dependent on precise SOH estimates. In order to model a Li-ion battery and determine SOH by means of an Extended Kalman Filter (EKF) algorithm, the Simscape Table- Based Battery block of MATLAB Simulink is employed. The model tracks the degradation behavior over time by combining voltage and current measurements with the battery's internal equivalent circuit. It is clear from the simulation results that the EKF-based estimator is accurate for table-based battery models, and that SOH degrades gradually. With this paradigm, embedded Battery Management Systems (BMS) can monitor battery health in real-time, improving diagnostics and prognostics without invasive sensors or costly hardware.