Paper Title
EV & Its Sub Systems: Design of Battery Management System

Electric vehicles have emerged as a revolutionary technology in recent times, with a Battery Management System (BMS) being the most critical component of an electric vehicle. The BMS acts as the brain of the battery pack in the automotive industry. Lithium-ion batteries have a high capacity for energy storage, and the BMS is responsible for managing the battery packs in electric vehicles. Its primary role is to accurately monitor the battery's condition, ensuring dependable operation and prolonging battery life.To accomplish this, the BMS tracks, estimates, and balances the battery pack's cells. The main objectives of this study are to monitor battery characteristics, estimate State of Charge (SoC) using three different algorithms, and balance the cells. The three algorithms to be implemented are Coulomb Counting, Extended Kalman Filter, and Unscented Kalman Filter. The Coulomb Counting method employs current as an input parameter, whereas the Extended and Unscented Kalman Filters consider current, voltage, and temperature values to calculate the state transition and measurement update matrix and estimate SoC. A comparative analysis of the results obtained from all three algorithms is conducted. MATLAB R2021a software is utilized for the simulation of different algorithms and SoC calculation. The BMS has three states: discharging phase, standby/resting phase, and charging phase. The simulation begins with SoC values of the cells set to 80%. At the end of the simulation, the maximum SoC values of Coulomb counting, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) achieved are 100%, 98.74%, and 98.46%, respectively. Additionally, cell balancing is performed over 6 cells of the battery pack after SoC estimation.