面向智能车辆的路面附着系数分段识别方法

Segmented Identification Method of Tire-Road Friction Coefficient for Intelligent Vehicles

路面附着系数是车辆主动控制系统的重要输入参数,其估计的准确性直接影响车辆动力学系统控制的性能,估算方法应满足实时、可靠和准确性高的要求。首先,建立3自由度车辆模型以及车轮受力模型;其次,分别采用了扩张状态观测器和自适应卡尔曼滤波对利用附着系数和滑移率进行识别与估计;最后,提出了一种路面附着系数的分段识别方法,可以有效识别路面附着系数,在估算过程中通过引入评价指标,减少了该方法的运算复杂度,提高了运行效率。仿真和实验结果表明,路面附着系数的估计误差在0.05以内,通过加入评价指标,算法的运行效率提高了21.1%,可以满足控制系统的实时性要求。

The tire-road friction coefficient is an important input parameter of the vehicle active control sys‐tem,the estimation accuracy of which directly affects the performance of the vehicle dynamics system control.The estimation method should meet the requirements of timeliness,reliability and high accuracy.Firstly,a 3DOF model and tire model of the vehicle are established.Secondly,a method of expansion state observer is used to estimate and identify the utilization of tire-road friction coefficient,and an adaptive Kalman filtering method is used to estimate and identify the slip rate.Finally,a segmented method for estimating the tire-road friction coefficient is proposed,which can effectively identify the tire-road friction coefficient.By introducing in the evaluation indicators in the esti‐mation process,the computational complexity of the method is reduced and the efficiency is improved.The simula‐tion and experimental results show that the estimation error of the tire-road friction coefficient is within 0.05,after introducing in the evaluation indicators,the operating efficiency of the algorithm is increased by 21.1%,which can meet the requirements of the control system.