基于EKF的18650锂电池SOC在线估算

On-line Estimation of 18650 Lithium Battery S0C Based on Extended Kalman Filtering

锂电池应用于新能源电动汽车及各类电子产品,但其荷电状态(SOC)估测一直是难点。本文在锂电池戴维宁模型的基础上结合传统安时计量法,采用扩展卡尔曼滤波(EKF)估算锂电池SOC。EKF估算方法的精度依赖于模型的状态空间表达式,文中针对不同SOC不同温度采用脉冲响应测试法,识别电池模型各项参数,估算过程中EKF采用查表法实时修正其自身系数。实验证明该方法对SOC的估测有较高的稳定精度,适用于在线估算。

Lithium batteries are used in new energy electric vehicles and all kinds of electronic products,but the state of charge（ SOC） estimation has been a difficult problem. Based on the lithium battery Thevenin model combined the traditional Ah counting method,using extended Calman filter（ EKF） estimate lithium battery SOC. The accuracy of EKF estimation method depends on the model＇s state space expression.This paper uses the impulse response test method to identify the parameters of the battery model,and the EKF method is used to modify its own coefficient in real time.The experiments identify that the proposed method has higher accuracy and can be applied to on-line estimation of SOC.