The state of charge (SOC) estimation is the core of any battery management system (BMS). However, the accurate SOC estimation is always a challenging task because it cannot be measured directly with sensors. An adaptive robust unscented Kalman filter (ARUKF) with recursive least square (RLS) is proposed to improve the robustness and accuracy of SOC estimation in this paper. In the proposed method, RLS is used for identification of the parameters of battery. Then, based on the identified parameters, ARUKF is designed to estimate the SOC of battery. The ARUKF is developed using embedding the unscented transformation (UT) technique and H∞ filtering into the unscented Kalman filter (UKF). The knowledge of noise distributions does not require in the proposed method, and the noises can be non-Gaussian, so it has less limitation in actual applications. In the proposed method, to improve more performance, the covariance of measurement and process noise are tuned. The process and measurement noise covariance can be adaptively tuned that improves the stability and accuracy of filter. The proposed method is evaluated under different real-time conditions. The results of proposed method are compared with those of extended Kalman filter (EKF) and UKF. The results show that the proposed method can achieve better SOC estimation accuracy, especially when the noise statistics are unknown and non-Gaussian.

荷电状态 (SOC) 估计是任何电池管理系统 (BMS) 的核心。然而，准确的 SOC 估计始终是一项具有挑战性的任务，因为它不能直接用传感器测量。为了提高SOC估计的鲁棒性和准确性，提出了一种具有递归最小二乘法(RLS)的自适应鲁棒无迹卡尔曼滤波器(ARUKF)。在所提出的方法中，RLS用于识别电池参数。然后，基于识别的参数，ARUKF 被设计用于估计电池的 SOC。ARUKF 是使用嵌入无迹变换 (UT) 技术和 H∞ 滤波到无迹卡尔曼滤波器 (UKF) 中开发的。该方法不需要噪声分布的知识，并且噪声可以是非高斯的，因此在实际应用中限制较少。在所提出的方法中，为了提高性能，对测量和过程噪声的协方差进行了调整。可以自适应地调整过程和测量噪声协方差，从而提高滤波器的稳定性和准确性。所提出的方法在不同的实时条件下进行评估。将所提出方法的结果与扩展卡尔曼滤波器（EKF）和UKF 的结果进行了比较。结果表明，所提出的方法可以达到更好的SOC估计精度，尤其是在噪声统计量未知且非高斯的情况下。将所提出方法的结果与扩展卡尔曼滤波器（EKF）和UKF 的结果进行了比较。结果表明，所提出的方法可以达到更好的SOC估计精度，尤其是在噪声统计量未知且非高斯的情况下。将所提出方法的结果与扩展卡尔曼滤波器 (EKF) 和 UKF 的结果进行了比较。结果表明，所提出的方法可以达到更好的SOC估计精度，尤其是在噪声统计量未知且非高斯的情况下。