Power battery SOC estimation method based on improved EKF algorithm

The invention provides a power battery SOC estimation method based on an improved EKF algorithm. A PNGV battery equivalent model is adopted, the model precision is improved, a capacity correction model related to the surface temperature of a battery and a current efficiency model related to coulombic efficiency are introduced on the basis of an extended Kalman filtering algorithm, the dynamic characteristics of the battery are more accurately represented, and correction of SOC estimation is realized. The SOC estimation method has the advantages of being high in estimation precision, small in error, capable of achieving the adjusting process of back-and-forth oscillation and high in anti-jamming capability, and SOC estimation can keep high precision in the whole charging and discharging interval and under the complex use condition. 本发明提出了一种基于改进EKF算法的动力电池SOC估算方法。采用了PNGV电池等效模型，改进了模型精度，并以扩展卡尔曼滤波算法为基础，引入了与电池表面温度有关的容量校正模型以及和库伦效率有关的电流效率模型，更准确地表征电池的动态特性，实现对SOC估算的校正。本发明具有估算精度较高、误差较小、且具有来回震荡的调节过程、抗干扰能力较强的优点，能够使SOC估算在整个