Modeling, Parameter, and State of Charge Estimation of Li-ion Battery for Electric Vehicle Application

Accurate modelling, parameter extraction, and State of Charge (SoC) estimation of Li-ion batteries are essential for grid-connected Electric Vehicle (EV) applications to ensure optimal energy management, extended battery lifespan, and grid stability. This paper presents a two-stage approach involving simulation-based SoC estimation and experimental parameter extraction for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations. Initially, a MATLAB/Simulink model is developed featuring a photovoltaic system, Battery Energy Storage System (BESS), and a grid-connected EV battery. SoC estimation is conducted using Coulomb Counting, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF). Separately, a pulse discharge test is performed using MATLAB and dSPACE to extract key internal parameters such as ohmic resistance, polarization resistance, capacitance, and open-circuit voltage. These parameters are then utilized in a MATLAB-based framework to estimate SoC under simulated load profiles. This study bridges simulation and experimental insights, contributing to more accurate and reliable battery management strategies for renewable-integrated EV systems.