摘要
针对车辆行驶过程中状态参数相互耦合,参数实时估计难度大的问题,对车辆行驶过程中关键参数估计问题进行研究。建立轮毂电机车辆纵向动力学模型,将整车质量与路面坡度解耦,分别建立整车质量递推最小二乘数学模型和路面坡度递推最小二乘数学模型,形成一种循环递推最小二乘法(circle recursive least square,CRLS),实现参数实时在线估计与相互校正,以达到更高精度的参数估计效果。通过建立Simulink参数估计模型和Carsim车辆模型进行了30%和50%坡度爬坡路面联合仿真,并将Simulink参数估计模型下载到实车控制器中进行实车爬坡试验,验证了参数估计算法在联合仿真和实车应用上的准确性与合理性。以上研究是最小二乘算法在参数估计领域应用的一种新实践,推进了无传感器参数获取的实现,也为车辆多参数实时控制打下了基础。
Due to the mutually coupling effect,it is difficult to estimate the parameters of vehicle state in real-time during driving.Based on the development platform for hub motor vehicle,the longitudinal dynamic model of the hub motor vehicle has been established by using the parameters in terms of speed/torque which could be accurately obtained in real-time.The whole vehicle weight recursive least square model and road slop recursive least square model have been utilised.The circle recursive least square(CRLS)is developed to realize parameters online estimation inreal-time and to achieve high quality of parameters estimation.The parameters estimation model and the whole vehicle model are established.The joint simulation is carried out in 30%/50%slop roads.Then the Simulink model is downloaded in VCU(Vehicle Control Unit)for real vehicle climbing tests.The validity and rationality for the parameters estimation algorithm are verified in such joint simulation and real vehicle applications.This research is a new trial of applying the method of recursive least square in parameter estimation.Obtaining the parameterswithout the assistance of sensors lays foundation of real-time control for multi-parameters vehicle.
作者
付翔
刘会康
黄斌
FU Xiang;LIU Huikang;HUANG Bin(Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan University of Technology,Wuhan 430070,China;School of Automotive Engineering,Wuhan University of Technology,Wuhan 430070,China)
