In order to achieve accurate state of charge (SOC) estimation of Lithium-Ion Battery, A method that dual Extended Kalman filters (DEKF) optimized by PSO-based Gray Wolf optimizer (MGWO) is proposed. A second-order equivalent circuit model with two resistor-capacitor branches is applied. The battery parameters are determined by battery test. Dual Extended Kalman filters are divided into state filter and parameter filter. Parameter filter is applied to adjust battery parameters online, state filter is applied to SOC estimation. Meanwhile, MGWO is applied to optimize the noise covariance matrix to improve the state estimation accuracy of SOC which reduces the linearization error from EKF. The results shows that the accuracy of algorithm is improved by adding online parameter identification and the optimization of the noise covariance matrix, meanwhile, the proposed method can adapt to the initial error well.

为了实现锂离子电池荷电状态（SOC）的准确估计，提出了一种基于PSO的灰狼优化器（MGWO）对双扩展卡尔曼滤波器（DEKF）进行优化的方法。应用了具有两个电阻-电容支路的二阶等效电路模型。电池参数由电池测试确定。双扩展卡尔曼滤波器分为状态滤波器和参数滤波器。参数滤波器用于在线调整电池参数，状态滤波器用于SOC估计。同时，应用MGWO优化噪声协方差矩阵，提高SOC的状态估计精度，减少EKF的线性化误差。结果表明，通过加入在线参数识别和噪声协方差矩阵的优化，提高了算法的精度，