Utilizing the Unscented Kalman Filter for Estimating State of Charge in Lithium-Ion Batteries of Electric Vehicles

Battery Management Systems (BMS) play a pivotal role in overseeing the performance and safety of Lithium-ion batteries, especially in applications such as electric vehicles (EVs). The accurate determination of the State of Charge (SOC) is paramount for the reliable operation of these batteries. SOC signifies the available energy remaining in the battery, which has a direct and significant influence on the operational range and functionality of the device it supplies power to. The intricate interplay of chemical reactions within lithiumion cells gives rise to a non-linear relationship between the Open Circuit Voltage (OCV) of the cells and their SOC. Additionally, SOC estimation is further complicated by environmental factors like temperature fluctuations and the dynamic nature of charging and discharging processes. To address these challenges, this study employs a 3-RC model to describe the electrical behavior of Lithium-ion batteries and introduces the Unscented Kalman Filtering (UKF) method for precise estimation of SOC. The effectiveness of this approach is evaluated through simulations using platforms like Simulink, enabling a comprehensive comparison between estimated and actual SOC values to ensure the safety and reliability of battery-powered systems.