基于增量误差的卡尔曼滤波算法全区间荷电状态估计

Entire Range State-of-charge Estimation Based on Incremental Error Using Kalman Filtering Algorithm

在电池管理系统领域,精确的荷电状态SOC(state-of-charge)估计是众多状态估计中最基础的技术之一。但在一些特定的SOC区间段内,便于实际应用的等效电路模型并不能很好地等效电池的特性,故基于传统卡尔曼滤波算法的SOC估计会出现精度下降的问题。提出一种基于增量误差的卡尔曼滤波算法,通过离线分析等效电路模型在各个SOC区间段内的性能优劣,得到各个SOC区间的噪声协方差控制表;将噪声协方差应用于对应的SOC区间段内,从而实现对全区间SOC的精确估计。实验表明在SOC低于20%的区间内,采用基于增量误差的卡尔曼滤波算法可以大大提高SOC估计精度。

In the field of battery management system,the accurate estimation of state-of-charge(SOC)is one of ba-sic techniques among many state estimations.However,the equivalent circuit model(ECM),which is convenient for pra-ctical applications,cannot well simulate the battery characteristics in some specific SOC ranges.Therefore,the accuracy of SOC estimation based on the traditional Kalman filtering algorithm will decrease.The accurate estimation in the entire SOC range is realized using a novel Kalman filtering algorithm based on incremental error.Based on the off-line analysis of the ECM performance in different SOC ranges,the control table for noise covariance in each SOC range is obtained.The specific noise covariance is applied in the corresponding SOC range so that the entire SOC range is accurately estimated using the proposed Kalman filtering algorithm.Experimental results showed that the estimation accuracy in the range with SOC lower than 20%can be significantly improved using the Kalman filtering algorithm based on incremental error.