Performance Validation of Electric Vehicle's Battery Management System under state of charge estimation for lithium-ion Battery

Electric Vehicles (EVs) have gained substantial attention in the recent years, since they are an efficient, sustainable and zero-carbon emitting means of transportation as compared to the conventional fossil-fuel powered vehicles. As EVs are becoming popular, the use of Lithium-ion (Li-Ion) batteries is exponentially increasing due to its good charge/discharge performance, high energy and current density and optimum power support. For safe operation of battery, precise estimation of the State of Charge (SOC) is necessary. SOC determines the residual charge accumulated in the battery and how further it can operate under specific conditions. This paper uses the Thevenin-equivalent circuit theory to model the transient behaviour of the Li-Ion battery and the SOC is evaluated using Coulomb counting and Extended Kalman Filter (EKF) methods. First, the battery is mathematically modelled and then the estimation is done via Coulomb counting and EKF in MATLAB/Simulink. A comparison of these two methods indicate that the SOC evaluation of the battery using EKF is more precise than Coulomb counting. The results show that the error is reduced by 1% when implemented via EKF.