Abstract: With the increasing precision requirements of battery management systems(BMS) in electric vehicles and energy sto-rage systems,the accurate estimation for the state of charge(SOC) of lithium-ion batteries becomes critical.To enhance the SOC estimation accuracy,this paper develops a new method using the Newton-Raphson optimized unscented Kalman filter(UKF) algorithm.Firstly,the mathematical model of lithium-ion battery is constructed based on a second-order RC equivalent circuit.In order to reduce the influence of noise’s initial value on SOC estimation accuracy,the Newton-Raphson algorithm is used to optimize the initial covariance matrices of process noise and observation noise in the UKF algorithm.This enhances the adaptability of the algorithm to the impact of noise.Finally,the incremental current experimental data are utilized to identify the parameters of the battery model.And the SOC estimation performance is validated through experiments conducted under constant current-rest and dynamic pressure test conditions.The experimental results show that the presented estimating algorithm has high precision and stability in both mean absolute error and root mean square error indices compared to the traditional UKF algorithm.This provides important technical support for optimizing battery management and ensuring the safe operation of lithium-ion batteries.

Key words: Lithium battery, SOC estimation, Unscented Kalman filter, Newton-Raphson algorithm