STUDY AND ANALYSIS OF BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLES

Abstract

The Battery Management System (BMS), a crucial part in charge of tracking, regulating, and maximising the condition of the car’s energy storage system, is essential to the efficiency and lifetime of electric vehicles. An essential component of battery management systems is battery state estimate. A system must regulate battery usage by taking into account two of the Battery Management System’s (BMS) many factors, such as State of Charge (SoC) and State of Health (SoH), in order to prevent battery failure and maintain battery lifetime. Battery health is measured by the State of Health, whereas the State of Charge gives the percentage of battery capacity in the BMS. To assess the fundamental quantities, like the state of charge or the state of health, an accurate model that captures the dynamic behavior of the battery is required. In order to do SoC estimation, the work first creates an estimating model utilizing a battery’s electrical circuit model (ECM), which is utilized to characterize the battery’s dynamic behavior and polarization characteristics. With the ECM technique, resistive and capacitive electrical components are used to simulate the voltage drop inside a battery. Lastly, the extended Kalman filtering technique is used to reduce the estimation error brought on by Gaussian noise. Finally, a MATLAB/Simulink test system is built to see how well the recommended methodology works.