基于指数冻结因子的锂电池SOC估算算法

纯电动汽车的荷电状态(SOC)表示电池组的剩余电量,其直接决定着驾驶员对电动车车剩余里程及对充放电等的判断。由于扩展卡尔曼滤波(EKF)法时其将其中的噪声按均值为零的高斯白噪声处理,因而使SOC估算精度不高乃至出现滤波发散。为防止该类情况发生,提出了一种基于指数冻结因子自适应滤波算法;该算法在Sage-Husa自适应扩展卡尔曼滤波(SHEKF)法的基础上引入发散判据,当有状态变量估算误差变大而发散时,对卡尔曼增益矩阵构造一个自适应指数冻结因子,有效防止了滤波发散,提高了系统稳定性。通过同传统的扩展卡尔曼滤波(EKF)法相比较,试验验证结果表明新方法具有更高的估算精度以及对滤波发散的有效控制.

Lithium Ion Battery SOC Estimation Algorithm Based on Index Freeze Factor

The state of charge of a pure electric vehicle(SOC)represents the remaining capacity of the battery pack,which directly determines the driver's determination of the remaining mileage of the electric vehicle and the judgment of charge and discharge.Due to the gaussian white noise with zero mean of Extended Kalman Filter(EKF)algorithm,thus the SOC estimation accuracy is not high or even appear filter divergence.In order to prevent this kind of situation,an adaptive filter algorithm based on index freeze factor is proposed.The new algorithm introduces a divergence criterion based on the Sage-Husa adaptive extended kalman filter(SHEKF)algorithm.When the state variable estimation error becomes larger and diverges,the adaptive index freeze factor is constructed for the Kalman gain matrix,effectively prevent the filter divergence,and improve the system stability.Compared with the traditional extended kalman filter(EKF)algorithm,the experimental results show that the new algorithm has higher estimation accuracy and the effective control of the filter divergence.