基于改进卡尔曼滤波的锂电池SOC估算研究

Research on SOC estimation of lithium battery based on improved Kalman filter

针对新能源汽车的驱动锂电池的剩余电量(SOC)估算,在对传统和新型SOC估算算法进行研究分析的基础上,讨论了卡尔曼滤波算法的运行机理,将其改进变换成拓展卡尔曼滤波算法(EKF)后同锂电池PNGV电路模型相结合对锂电池进行SOC估算。来弥补传统安时法在实际应用中随着估算时间增加而误差增大的缺点。接下来将本文的SOC联合估算算法同传统Ah方法进行综合比较,通过Matlab/Simulink中建立电池的综合仿真模型对SOC算法进行评估和仿真,对其准确性进行验证,证明了算法在面对噪声干扰时比传统的SOC估算算法具有更好鲁棒性和准确性。

Based on the analysis of the traditional and new SOC estimation algorithms,the Kalman filter algorithm is transformed into extended Kalman filter algorithm (EKF) and then combined with the PNGV circuit model of lithium battery to estimate the SOC of lithium battery.The design could make up for the shortcomings of the traditional Ah method that the error increases with the increase of estimation time in practical application.Furtherly,the SOC joint estimation algorithm in this paper is compared with the traditional Ah method.The integrated simulation model of batteries is established in Matlab / Simulink to evaluate and simulate the SOC algorithm.The accuracy of the algorithm is verified.It is proved that in the face of noise interference,the algorithm has better robustness and accuracy than the traditional SOC estimation algorithm.