This study proposes a two-stage framework for real-time estimation of tire–road friction coefficient of a vehicle on the basis of lateral dynamics of the vehicle. The estimation framework employs a new cascade structure consisting of an extended Kalman filter and two unscented Kalman filters to reduce the computational burden. In the first stage, extended Kalman filter is utilized to estimate lateral velocity of the vehicle and thereby both the front and rear tires’ side-slip angles. In the second stage, a two–unscented Kalman filters sub-framework is formulated in sequence to observe both the front- and rear-axle tire forces, and to subsequently identify their respective tire–road friction coefficient, regarded as two unknown states. All the measured signals required in the study could be realized from the conventional on-board sensors. Typical double-lane change and single-lane change maneuvers were designed and the developed algorithm was verified through CarSim–MATLAB/Simulink software platform considering high-, mid-, and low-friction road conditions. The simulation results show that the proposed method can yield accurate and rapid estimations of the tire–road friction coefficient for mid- and low-friction road conditions even under a single-lane change maneuver, although double-lane change maneuver is needed to accurately estimate the tire–road friction coefficient for high-friction road condition.

中文翻译：

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基于车辆横向动力学的轮胎-道路摩擦系数实时估计

本研究提出了一种基于车辆横向动力学实时估计车辆轮胎-道路摩擦系数的两阶段框架。估计框架采用新的级联结构，由一个扩展卡尔曼滤波器和两个无迹卡尔曼滤波器组成，以减少计算负担。在第一阶段，扩展卡尔曼滤波器用于估计车辆的横向速度，从而估计前后轮胎的侧滑角。在第二阶段，依次制定双无味卡尔曼滤波器子框架来观察前后轴轮胎力，并随后识别它们各自的轮胎 - 道路摩擦系数，视为两个未知状态。研究中所需的所有测量信号都可以通过传统的车载传感器实现。设计了典型的双车道变换和单车道变换机动，并通过CarSim–MATLAB/Simulink软件平台对所开发的算法进行了验证，该平台考虑了高、中、低摩擦路况。仿真结果表明，即使在单车道变换机动下，所提出的方法也可以准确快速地估计中低摩擦路面条件下的轮胎 - 道路摩擦系数，尽管需要双车道变换机动来准确估计高摩擦路面条件下的轮胎-路面摩擦系数。