采用改进最大相关熵自适应迭代容积卡尔曼滤波 算法的锂离子电池荷电状态估计

In response to the issues of instability and low accuracy in estimating the state of charge(SOC)of lithium-ion batteries under non-Gaussian noise interference traditional filtering algorithms, an innovation maximum correlation-entropy criterion adaptive iterated cubature Kalman filtering algorithm(IMCC-AICKF)is proposed for SOC estimation of lithium-ion batteries. The proposed algorithm combines the weighted least squares method with the maximum correlation-entropy criterion(MCC)to define a new cost-weight function as the optimization criterion. This approach aids in reducing filtering errors by optimizing the minimum noise covariance matrix to reduce filtering errors and stability of long-term filtering. Subsequently, by integrating with the adaptive iterative covariance Kalman filter(AICKF), the process noise covariances and measurement noise covariances are updated to enhance estimation accuracy and robustness. Based on two sets of battery data and under non-Gaussian noise interference, the proposed algorithm is applied to estimate the SOC of the batteries. The simulation results demonstrate that compared to cubature Kalman filtering(CKF)and innovation maximum correlation-entropy criterion cubature Kalman filtering(IMCC-CKF), the IMCC-AICKF algorithm yields the smallest maximum absolute error(MaxAE), mean absolute error(MAE), and root mean square error(RMSE)in SOC estimation, with both MAE and RMSE below 1%. Additionally, even with initial value errors, IMCC-AICKF can accurately converge to the true values, demonstrating good robustness. The proposed algorithm achieves more accurate estimation under non-Gaussian noise, providing a high-precision and robust method for SOC estimation.