扩展卡尔曼滤波算法的锂电池SOC估算

SOC estimation of lithium battery based on extended Kalman filter

以提高锂电池的荷电状态估算精准度为目的,提出基于扩展卡尔曼滤波算法的锂电池SOC估算方法。忽略放电率的状态,仅针对电池组的欧姆极化、浓差极化、电化学极化及工作温度等影响因素建立锂离子电池组等效电路模型,采用电量放电倍率下的SOC与温度的关系拟合数据,获取电池温度对参数辨识的影响,将表征温度效应的等效电阻增加在二阶RC电池等效模型中,采用扩展卡尔曼滤波算法,将非线性系统转化为线性系统后,利用卡尔曼滤波实现锂电池SOC的最优估算。实验结果表明,该方法的估算均方根误差仅为0.025,估算单次平均消耗仅为5.81×10^(-4)s;具有良好的估算跟踪性及适用性。

In order to improve the accuracy of SOC estimation of lithium battery, a novel SOC estimation method based on extended Kalman filter was proposed. Ignoring the state of discharge rate, the equivalent circuit model of lithium-ion battery pack was established only for the influencing factors such as ohmic polarization, concentration polarization, electrochemical polarization and working temperature. The relationship between SOC and temperature under charge discharge rate was used to fit the data, and the influence of battery temperature on parameter identification was obtained. The equivalent resistance of temperature effect was increased to equivalent value of second-order RC battery in the model, the extended Kalman filter algorithm was used to transform the nonlinear system into a linear system, and the optimal SOC estimation of lithium battery was realized by Kalman filter. The experimental results showed that the root mean square error of this method was only 0.025, and the average consumption of single estimation was only 5.81 × 10^(-4)s. It has good estimation tracking and applicability.