无迹卡尔曼滤波对锂电池荷电状态估算的研究

Research on SOC Estimation of Lithium Battery by Unscented Kalman Fliter

由于动力锂电池参数具有受外界干扰影响大、电池模型非线性的特点,现有的荷电状态(SOC)估算方法并不能完全满足精度和实时性的需要。在综合考虑模型的精确性和实际工程计算复杂程度后,提出使用经验公式模型,在模型的基础上采用无迹卡尔曼滤波(UKF)算法对电池SOC进行估算。通过对比动力锂电池放电实验得到的数据,检验算法估算效果。实验结果表明:UKF算法能够准确跟踪动力锂电池放电变化情况,对动力锂电池SOC的估算误差在2%左右,相比于传统算法在精度上有较大的提高。

Because power lithium battery parameters were susceptible to external disturbance and the battery model was non-linear,the existing state of charge( SOC) estimation method cannot fully meet the needs of precision and real-time. Considering the accuracy of the model and the complexity of the actual engineering calculation,the empirical model was proposed. On the basis of the empirical model,unscented Kalman filter( UKF) algorithm was used to estimate SOC. The estimated effect of the algorithm was tested by comparing with the power lithium battery discharge test data. The results show that the UKF algorithm can accurately track the discharge changes of power lithium battery. The SOC estimated error of the power lithium battery is about 2%.The precision of the UKF algorithm is improved greatly compared with the traditional algorithm.