基于QKF的隧道电力工程车辆锂电池SOC估计算法

SOC estimation algorithm of lithium-ion battery for tunnel electric power engineering vehicle based on QKF

针对隧道电力工程车辆的电池荷电状态(SOC)估计问题,提出一种新的非线性滤波算法-求积分卡尔曼滤波器(QKF),用于对SOC的估计。QKF使用统计线性回归的方法,通过一套参数化高斯密度的高斯-厄米特积分点来线性化非线性函数,该算法的数值鲁棒性高,估计精度高。运用二阶等效电路模型对锂离子电池进行建模,构建模型的状态空间方程后,运用QKF算法对电池的SOC进行估计。仿真实验表明,QKF对SOC的估计误差很小,低于1%,表明QKF算法是一种很好的估计电池SOC的方法,具有较高的精确度。

Aiming at the estimation of State of Charge(SOC)of electric vehicles in tunnels,a new nonlinear filtering algorithm,Quadrature Kalman Filter(QKF),is proposed to estimate SOC.QKF uses the statistical linear regression method to linearize the nonlinear function through a set of parameterized Gaussian Hermite integral points.The algorithm has high numerical robustness and high estimation accuracy.The second-order equivalent circuit model is used to model the lithium-ion battery,and the state space equation of the model is established.Then the QKF algorithm is used to estimate the SOC of the battery.The simulation results show that the estimation error of QKF is very small,less than 1%.It shows that the QKF algorithm is a good method to estimate the SOC of the battery,and it has high accuracy.