This article presents an approach to lithium-ion battery state of charge estimation based on the quadrature Kalman filter. Among the existing state of charge estimation approaches, the extended Kalman filter–based state of charge and unscented filter–based state of charge algorithms are influenced by the linearization or the solution of sigma points. The proposed quadrature Kalman filter–based state of charge algorithm avoids these problems. Specifically, the battery system equations are built based on the second-order resistance–capacitance equivalent circuit model, and the parameters are identified according to the hybrid pulse power characterization discharging test. Then, the quadrature points and corresponding weights are defined by the Gauss–Hermite quadrature rule, and the Kronecker tensor product is adopted to solve the points of multivariate. In addition, the stability of quadrature Kalman filter–based state of charge is verified. Finally, the simulation is carried out under the discharging and urban dynamometer driving schedule condition, which demonstrates that the quadrature Kalman filter–based state of charge algorithm has a better performance compared with extended Kalman filter–based state of charge and unscented filter–based state of charge.

本文提出了一种基于正交卡尔曼滤波器的锂离子电池充电状态估计方法。在现有的荷电状态估计方法中，基于线性化或sigma点的解决方案会影响基于扩展卡尔曼滤波器的荷电状态和基于无味滤波器的荷电状态算法。提出的基于正交卡尔曼滤波器的电荷状态算法避免了这些问题。具体来说，基于二阶电阻-电容等效电路模型建立电池系统方程，并根据混合脉冲功率特性放电测试确定参数。然后，用高斯-赫米特正交规则定义正交点和相应的权重，并采用Kronecker张量积来求解多变量点。此外，还验证了基于正交卡尔曼滤波器的电荷状态的稳定性。最后，在放电和城市测功机行驶时间表条件下进行了仿真，这表明与基于扩展卡尔曼滤波器的荷电状态和基于无味滤波器的状态相比，基于正交卡尔曼滤波器的荷电状态算法具有更好的性能。收费。