基于二阶RC锂电池模型的自适应卡尔曼SOC估算

Improved Kalman SOC Estimation Based on Second-order RC Lithium Battery

为了更加精确地估算锂电池荷电状态,建立能够反映电池动态工作特性的二阶RC等效模型。在模型参数辨识的基础上,先用数学分析、电路模型构建相关状态方程与观测方程,再结合扩展卡尔曼滤波算法,针对状态噪声与观测噪声的不确定干扰,提出一种自适应调整方案。通过锂电池放电实验,结合MATLAB仿真分析,验证了自适应调整后的卡尔曼滤波算法可更精确地预估锂电池荷电状态,与扩展卡尔曼滤波算法相比,该算法降低了约2.31%的误差。

In order to estimate the state of charge(SOC) of lithium battery more accurately,a second-order RC equivalent model which can reflect the dynamic working characteristics of the battery is established.Based on the model parameter identification,we made mathematical analysis and circuit analysis to establish these equations.Based on the equation of state,observation equation and the filtering algorithm of extended Kalman,the disturbance of state noise and observed noise is proposed.An adaptive adjustment strategy for state noise and observed noise is proposed.Through the discharge experiment of lithium battery,combined with MATLAB the simulation analysis proves that the adaptive adjusted Kalman filter can predict the state of charge of the lithium battery more accurately,which is about 2.31% lower than the original extended Kalman filter algorithm,and improves the state of charge of the lithium battery.