一种GA-DKF的锂离子电池SOC估计

SOC estimation of lithium-ion battery based on GA-DKF

提出了一种基于遗传算法优化双卡尔曼滤波(GA-DKF)的方法进行电池荷电状态(SOC)估计。分别利用遗传算法优化模型参数辨识中卡尔曼滤波的噪声协方差矩阵,以及SOC估计中无迹卡尔曼滤波(UKF)的噪声协方差矩阵。搭建试验平台,依据不同电流倍率下的放电实验数据,得到模型参数动态辨识的结果,分析GA-KF算法辨识参数的有效性。通过不同工况下试验结果、仿真结果以及传统最小二乘法(LS)辨识结果的对比,表明提出的方法能够有效提高锂电池模型精度。最后,采用遗传算法优化双卡尔曼滤波(GA-DKF)进行SOC估计,分别验证该模型在动态工况下SOC估计的精度和鲁棒性。结果表明:该模型不仅具有较高的估计精度,还能克服不同初始SOC的误差,具备良好的鲁棒性。

A method based on genetic algorithm to optimize the double Kalman filter(GA-DKF) is proposed to estimate the battery state of charge(SOC).The genetic algorithm is used to optimize the noise covariance matrix of the Kalman filter in the model parameter identification and the noise covariance matrix of the Unscented Kalman Filter(UKF) in the SOC estimator.An experimental platform is built, according to the discharge experimental data at different current rates, the dynamic identification results of model parameters are obtained, and the effectiveness of parameter identification by GA-KF algorithm is analyzed.The comparison of test results, simulation results, and traditional least squares(LS) identification results under different working conditions show that the proposed method can effectively improve the accuracy of the lithium battery model.Finally, the genetic algorithm is used to optimize the double Kalman filter(GA-DKF) for SOC estimation, the accuracy and robustness of SOC estimation of the model under dynamic conditions are verified respectively.The results show that the model not only has a high estimation accuracy, but also can overcome the errors of different initial SOC.This method has good robustness.