基于多新息最小二乘和多新息扩展卡尔曼滤波算法的锂电池 SOC 估计

The existing State of Charge (SOC) estimation methods assume constant parameters for the battery model and do not consider the dynamic changes in these parameters, resulting in imprecise SOC estimates. In view of this limita⁃ tion, the paper introduced an algorithm that combines online identification of battery model parameters with SOC estima⁃ tion. Based on the second-order RC equivalent circuit model, it used Multi Innovation Least Squares (MILS) algorithm to identify the parameters in the lithium-ion battery model online, so as to modify the battery model in real time. At the same time, based on the modified battery model, it estimated the battery state of charge through Multi Innovation Ex⁃ tended Kalman Filter (MIEKF) algorithm. MILS algorithm can solve the problem of initial error accumulation in the pro⁃ cess of online parameter identification, and can realize online accurate identification of model parameters. MIEK algo⁃ rithm combines multi-innovation theory and Kalman filter theory, adds forgetting factor to weaken historical data and cor⁃ rect weight, solves the problem of data oversaturation, and has high accuracy and convergence. The experimental results show that, when identifying the parameters of the battery model, the Root Mean Square Error (RMSE) of the MILS algo⁃ rithm is 1. 4mV, the RMSE of the RLS algorithm is 1. 9mV, and the estimation accuracy is improved by 26. 3%. For the SOC estimation after parameter identification, the RMSE estimated by the MIEKF algorithm is 0. 0037, while the RMSE estimated by the EKF and AEKF algorithms are 0. 0073 and 0. 0052, respectively. The MIEKF algorithm improves the estimation accuracy by 49. 31% compared to the EKF algorithm and by 28. 84% compared to the AEKF algorithm. Moreover, in the case of an incorrect initial SOC value, the proposed algorithm can converge to the true value after about 30 seconds of battery operation. The algorithm proposed in the paper is an effective estimation method with high accu⁃ racy and good robustness.