SOC Estimation of Lithium Battery Based on Improved Unscented Kalman Filter

An estimation approach based on adaptive unscented Kalman filter (AUKF) was presented to address the issue of low accuracy of state of charge (SOC) estimation of lithium battery with unscented Kalman filter (UKF) in the event of noise uncertainty and complex working conditions. The least squares method was employed to identify the parameters in the lithium battery's equation of state, which was created using the Thevenin equivalent circuit model. When data is gathered under experimental conditions and compared to traditional UKF, the estimation error may be kept around 4%, demonstrating the model's efficacy.