An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

For lithium-ion batteries, the state of charge (SOC) of batteries plays an important role in the battery management system, and the accuracy of the battery model and parameter identification is the basis of SOC estimation. Considering that the system has inevitable steady-state errors and the influence of random noise on SOC estimation results under dynamic conditions, this paper proposed an improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering (PCFFRLS-MCAEKF) algorithm for high-precision state-of-charge estimation of lithium-ion batteries. The experimental results show that the proportional control forgetting factor recursive least square algorithm has higher parameter identification accuracy under HPPC and BBDST conditions. Under HPPC working conditions, the root mean square error of PCFFRLS-MCAEKF algorithm is reduced by 1.275%, 0.687%, and 0.549% compared with FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithm, and the average absolute error is reduced by 0.71%, 0.537%, and 0.11%. Under BBDST working conditions, the SOC estimation result of PCFFRLS-MCAEKF algorithm is closer to the real SOC, which is consistent with the result obtained under HPPC working conditions. The experimental results show that under HPPC and BBDST working conditions, the PCFFRLS-MCAEKF algorithm can better improve the accuracy and robustness of SOC estimation than FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithms.