汽车质量与道路坡度串行估计方法

SERIAL ESTIMATION OF VEHICLE MASS AND ROAD GRADE

汽车质量与道路坡度是汽车主动安全控制系统的重要参数.提出一种汽车质量与道路坡度串行估计算法.根据汽车质量与道路坡度变化的快慢进行分层串行估计,将缓慢变化的汽车质量作为第一层的估计输出,将快速变化的道路坡度作为第二层的估计输出.基于纵向动力学,首先由轮胎驱动力矩与横摆角速度通过递推最小二乘法(Recursive Least Squares,RLS)算法进行第一层的汽车质量估计;接着将估计得到的汽车质量代入至第二层牛顿迭代法进行道路坡度估计.与传统的自适应估计方法相比,提出的算法可以减少实时估计参数的耦合效应,且不需要额外的传感器;最后通过仿真及模型车辆道路试验对所提出的算法进行验证,仿真及试验结果表明:所提出的辨识算法能够准确实时地估计汽车质量与道路坡度.

Vehicle mass and road slope are important parameters of active safety control system.This paper presents a serial estimation algorithm for vehicle mass and road slope.The vehicle mass and road slope are estimated in a hierarchical and serial manner according to changing speed.The slowly varying vehicle mass is taken as the estimated output of the first layer,and the rapidly varying road slope is taken as the estimated output of the second layer.The estimation algorithm is based on longitudinal dynamics.Firstly,the vehicle mass of the first layer is estimated through recursive least squares(RLS)algorithm based on tire driving moment and angular velocity,and then the estimated vehicle mass is substituted into the second-layer Newton iteration method for road slope estimation.Compared with the traditional adaptive estimation method,the proposed algorithm can reduce the coupling effect of real-time estimation parameters and does not require additional sensors.Finally,the algorithm is verified through simulation and electric control model vehicle road test,and the simulation and test results show that the proposed identification algorithm can accurately estimate vehicle mass and road slope in a real-time manner.