Presently, environmental pollution risks and greenhouse effects are the most important concerns of the researchers. Lithium-ion (Li-ion) batteries are mainly used due to their low self-discharge, high life cycle, lightweight and high power density. The primary role of the Battery Management System (BMS) is to calculate and determine critical parameters such as State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). The battery model captures the battery’s dynamics, encompassing four primary components: an Open Circuit Voltage (OCV) source, two RC networks, and a resistor. In this work, the Enhanced-self-correcting (ESC) model is used for mathematical modelling. Model-based estimators execute algorithms that leverage sensed measurements to estimate the concealed internal state of a dynamic system. ESC model includes all parameters that are missed in the voltage-based method’s Trio model that as hysteresis, voltage drop, etc. The parameters are iteratively adjusted to converge to their actual values, which are subsequently employed to estimate the State of Charge (SOC) of the cell using the Extended Kalman Filter (EKF) algorithm, grounded in the dynamics of the battery model. Through successive update and prediction stage iterations, the process progresses to minimize the discrepancy between the real SOC and the estimated SOC. It is mainly focused on SOC estimation of BMS which is one of the complex parameters to compute and is never 100% accurate. The approximate estimation of SOC with some tolerance limit is found by using certain propagation predictions. The modelling is accomplished utilizing MATLAB/Simulink.