针对现有SOC（荷电状态）估计方法中电池模型参数恒定，没有考虑电池模型参数的动态变化，导致SOC的估计不够精准的问题，文中提出了一种基于电池模型参数在线辨识与SOC估计联合的算法。在二阶RC等效电路模型基础上，采用多新息最小二乘（Multi Innovation Least Squares，MILS）算法对锂离子电池模型中的参数进行在线辨识，从而对电池模型进行实时修正；同时基于修正后的电池模型，采用多新息扩展卡尔曼滤波（Multi Innovation Extended Kalman Filter，MIEKF）算法对电池荷电状态进行估计。MILS算法可以解决在线参数辨识过程中的初始误差累积问题，能够实现模型参数的在线精准辨识，MIEKF算法融合了多新息理论和卡尔曼滤波理论，加入了遗忘因子以削弱历史数据并修正权重，解决了数据过饱和问题，具有较高的准确性和收敛性。实验结果表明，在对电池模型进行参数辨识时，MILS算法、RLS算法辨识的均方根误差分别为1.4、1.9 mV，MILS算法相比RLS算法的估计精度提高了26.3%；对于参数辨识后SOC的估计，MIEKF算法估计的均方根误差为0.003 7，EKF算法、AEKF算法估计的均方根误差分别为0.007 3、0.005 2，MIEKF算法比EKF算法的估计精度提高了49.31%，比AEKF算法的估计精度提高了28.84%；并且在给定SOC初值错误的情况下，文中所提出算法在电池开始工作后30 s左右就能够收敛到真实值，是一种精度高而且鲁棒性好的有效估计方法。

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 limitation, the paper introduced an algorithm that combines online identification of battery model parameters with SOC estimation. 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 Extended Kalman Filter (MIEKF) algorithm. MILS algorithm can solve the problem of initial error accumulation in the process of online parameter identification, and can realize online accurate identification of model parameters. MIEK algorithm combines multi-innovation theory and Kalman filter theory, adds forgetting factor to weaken historical data and correct 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 algorithm is 1.4 mV, the RMSE of the RLS algorithm is 1.9 mV, and the estimation accuracy is improved by 26.3%. For the SOC estimation after parameter identification, the RMSE estimated by the MIEKF algorithm is 0.003 7, while the RMSE estimated by the EKF and AEKF algorithms are 0.007 3 and 0.005 2, 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 accuracy and good robustness.