基于采样点卡尔曼滤波的动力电池SOC估计

动力电池荷电状态(SOC)的快速精确估计是电池能量管理系统的核心技术。针对动力电池这一动态非线性系统,提出了电池过程模型的具体改进方法,以使其可以适应不同放电速率和不同温度条件对动力电池SOC的影响;给出了利用采样点卡尔曼滤波进行电池SOC估计的具体步骤;最后,分析了采样点卡尔曼滤波在SOC估计精度、收敛速度、算法复杂度及鲁棒性等方面的性能。实验表明,采用采样点卡尔曼滤波算法可以快速地完成动力电池SOC的精确估计,误差在5%左右;模型参数的合理微调几乎不影响算法的准确性,表明了算法具有一定的鲁棒性。

Sigma Point Kalman Filter Based SOC Estimation for Power Supply Battery

In the power management system for a power supply battery,the fast accurate estimate of the state of charge(SOC)is the key technique.For the inherent dynamic and nonlinear property of a power supply battery,firstly,an improved process model is proposed to compensate for the influence of the varying discharge rate and the different working temperature.And then,the detail procedures and algorithms for battery SOC estimation based on the sigma point Kalman filter are given.Finally,the accuracy,the convergence rate,the time complexity and the robustness of the proposed method are analyzed.Experiments show that,the sigma point Kalman filter based method can be used to estimate the SOC quickly and accurately with an estimate error of 5%.On the other hand,small adjustment of the model parameters does not influence the accuracy of the proposed method,which shows the robustness of the sigma point Kalman filter based method.