State-of-Charge Estimation of Lithium Battery Based on Square Root Unscented Kalman Filter Algorithm

Precise state-of-charge estimation of lithium batteries is a requirement to make sure the battery management system can be operated reliably. This paper establishes a second-order RC equivalent circuit model at first. Then parameter identification are done according to the deviation-compensated least squares method. A square root unscented Kalman filter algorithm is used for state-of-charge estimation at different initial values. The experimental outcomes mirror that the state-of-charge estimation based on the square root unscented Kalman filter algorithm has higher accuracy, robustness and faster convergence speed.