In order to identify the road friction coefficient of the left and right sides of the vehicle more accurately, this paper presents an estimation framework based on a novel tyre model and modified square-root cubature Kalman filter (SCKF). To begin with, a novel tyre model is proposed by adaptively calculating the longitudinal and lateral stiffness and the effective friction coefficient. The tyre forces calculated by this model are more accurate than those calculated by the Brush model. Then, to avoid the influence of abnormal measurement noise on the estimation effect, we develop an improved SCKF (ISCKF) algorithm based on the maximum correntropy criterion. The algorithm can update the measurement noise covariance adaptively. Furthermore, a real-time estimation scheme of road friction coefficient is designed by combining the vehicle dynamics model with the proposed novel tyre model and ISCKF algorithm. Finally, the performance of the presented method is verified by the co-simulation of CarSim and MATLAB/Simulink. The results show that the designed estimation scheme not only has excellent robustness in the case of abnormal measurement noise interference but also has good adaptability to the uncertainty of road friction coefficients distribution.

为了更准确地识别车辆左右两侧的道路摩擦系数，本文提出了一种基于新型轮胎模型和改进的平方根容积卡尔曼滤波器（SCKF）的估计框架。首先，通过自适应计算纵向和横向刚度以及有效摩擦系数，提出了一种新的轮胎模型。此模型计算的轮胎力比 Brush 模型计算的更准确。然后，为了避免异常测量噪声对估计效果的影响，我们提出了一种基于最大相关熵准则的改进SCKF（ISCKF）算法。该算法可以自适应地更新测量噪声协方差。此外，将车辆动力学模型与所提出的新型轮胎模型和ISCKF算法相结合，设计了一种道路摩擦系数实时估计方案。最后，通过 CarSim 和 MATLAB/Simulink 的联合仿真验证了所提方法的性能。结果表明，所设计的估计方案不仅在测量异常噪声干扰的情况下具有良好的鲁棒性，而且对道路摩擦系数分布的不确定性具有良好的适应性。