基于极化效应PNGV模型的动力锂电池SOC估算

Lithium Battery SOC Estimation Based on Polarization Effect PNGV Model

为提高锂电池荷电状态的估算精度及模拟锂离子电池实际充放电特性的准确性,本文通过改进现有的PNGV等效电路模型,在PNGV模型基础上,增加一节RC并联模块,使模型更好的反映电池的极化效应。同时,通过实验获取电池充放电特性,为卡尔曼滤波器提供精确的参数,并提出了电池的开路电压曲线模型,通过Matlab拟合验证满足精度要求。最后采用扩展卡尔曼滤波(extended kalmanfilter,EKF)算法对锂电池荷电状态(state of charge,SOC)进行估算,并与安时积分法进行比较。实验结果表明,估算最大误差不超过4.5%,平均不超过3%,提高了SOC的估算精度。该研究为电动汽车运行工况提供了理论依据。

Lithium-ion batteries are superior to other types of batteries in electric vehicle applications due to their safety, fast charging capability and long cycle life.In order to improve the estimation accuracy of the state of charge of the lithium battery and the accuracy of the model to simulate the actual charge and discharge characteristics of the lithium ion battery. Firstly, by improving the existing PNGV equivalent circuit mathematical model, based on the PNGV model, by adding an RC parallel module, the model can better reflect the polarization effect of the battery. Secondly, the charge and discharge characteristics of the battery are obtained through experiments to provide accurate parameters for the Kalman filter, and an open-circuit voltage curve model of the battery is proposed, and the accuracy is satisfied by the Matlab fitting verification.Finally, the EKF algorithm is used to estimate the SOC of the lithium battery and it is compared with the Ah integration method. The experimental verification verifies that the maximum error is less than 4.5%, and the average is less than 3%.Experimental verification shows that the SOC estimation accuracy has been effectively improved.