Estimation of Lithium-Ion Battery State-of-Charge Using an Unscented Kalman Filter

The document discusses the modeling and the requirements for identifying and estimating system parameters. Based on experimental tests involving the discharge of a lithium-ion (Li-ion) cell and subsequent analysis, we have developed a precise mathematical model (Thevenin Model) that accurately represents the cell's dynamics. This model includes terms that describe the dynamic parameters of the cell. The SOC estimation is proposed using an unscented Kalman filter (UKF) based on the Thevenin model (TM) and the relationship between SOC and VOCV. Recently, Kalman filters have gained popularity in deploying and constructing various estimators for SOC. When compared to the conventional extended Kalman filter, the UKF uses an unscented transform to address state estimation problems, providing the precision of Taylor expansion to track the later estimation of the internal state. Consequently, the accuracy of SOC estimation is improved, leading to better tracking accuracy and faster convergence. The experimental results were used to test and affirming the performance superiority of the employed SOC estimation method, and were implemented using MATLAB.