基于不同模型下EKF算法的锂离子电池SOC估计

SOC Estimation of Lithium-ion Battery Based on EKF Algorithm under Different Models

动力电池的准确建模及荷电状态(SOC)的精准估计对提高电池的利用效率、延长使用寿命具有重要意义。本文对锂离子电池进行等效电路模型的建立并通过HPPC测试对模型进行参数辨识。利用Matlab软件,基于两种模型开展扩展卡尔曼滤波算法下的电池SOC估计精度对比实验研究。结果表明,在同一实验条件下,EKF-Ah SOC估计方法能够有效提高电池SOC的估计精度;与Rint模型相比,基于Thevenin模型的EKF-Ah SOC估计精度得到显著提高,SOC估计最大绝对误差为1.91%。

It is great significant for improving the utilization efficiency of battery and prolonging its service life of accurate modeling and estimation of SOC of power battery.In this paper,the equivalent circuit model of lithium-ion battery is established and the parameters of the model are identified by HPPC test.Matlab software was used to carry out a comparative experimental study of battery SOC estimation accuracy under the extended Kalman filter algorithm based on the two models.The results show that under the same experimental conditions,the EKF-AH SOC estimation method can effectively improve the estimation accuracy of battery SOC.Compared with Rint model,the SOC estimation accuracy of EKF-AH based on Thevenin model is significantly improved,and the maximum absolute error of SOC estimation is 1.91%.