重型车辆具有重心高,装载量大,高宽比相对乘用车较大等特点,导致其侧翻稳定极限较低,极易发生侧翻事故。建立重型车辆侧翻预测模型,利用车辆实车试验数据离线辨识技术,辨识出3自由度车辆模型中的关键参数,然后利用改进后的车辆模型进行在线侧翻危险预测及控制,实现车辆动态侧翻特性精确预测。在此基础上,将卡尔曼滤波技术融入到改进侧翻预测时间(Time to rollover,TTR)侧翻预警算法中,选取车辆的横向载荷转移率作为侧翻极限判据,根据当前车辆状态预测未来3 s车辆的侧翻危险程度,并实时计算TTR值,一旦TTR值满足侧翻条件,系统自动触发预警装置。利用侧翻预警系统车载试验平台,对侧翻预警控制系统进行验证。侧翻预警场地试验结果表明:所开发的重型车辆侧翻预警系统可以准确有效地进行车辆侧翻危险程度预警,达到预期的开发设计目标。
A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.
