基于改进EKF的飞机蓄电池在线SOC估计方法

蓄电池荷电状态(state of charge,SOC)是电池管理系统最为重要的参数之一,由于飞机蓄电池工作环境恶劣复杂,具有较强的非线性,给蓄电池的在线 SOC估计带来较大的困难。以提高复杂应力条件下飞机蓄电池在线 SOC估计精度为目的,采用性能测试实验对蓄电池性能参数的温度、放电率特性进行研究,并提出递推最小二乘法与扩展卡尔曼滤波算法结合的改进 EKF方法,实现蓄电池等效电路模型参数的在线辨识以及蓄电池在线 SOC 的估计。上述方法通过物理实验进行了验证,实验结果表明,改进后 EKF方法的 SOC 估计误差小于0.5%,估计精度获得明显提高。

Method of aircraft battery on line SOC estimation based on improved EKF algorithm

State of charge (SOC) is one of the most important parameters for the battery management system. Due to the aircraft battery complex working conditions and strong nonlinear, it brings difficulties for the battery online SOC estimation. In order to improve the aircraft battery online SOC estimation accuracy under complicated stress condition, the temperature and discharge rate characteristics of battery performance parameters were researched. Then, this paper puts forward a improved EKF method which combine EKF algorithm with RLS algorithm. This method not only can realize online identification of the equivalent circuit model parameter, but also can realize the on line SOC eatimation. This method was physical verified through experimental, and the experimental results show that the improved EKF method can enhance the online SOC estimation obviously, the accuracy can reach above 0.5%.