基于分段递推最小二乘估计的汽车质量辨识试验

Experimental Research on Partitioned Recursive Least Squares Estimation of Vehicle Mass

基于电动轮驱动电动汽车平台道路试验,对一种新的汽车质量辨识算法进行了研究.该方法根据加速度传感器能够测量沿测量轴的重力分量的特点,排除了坡度对质量辨识的影响;根据加速度分段方法,分别利用两段递推最小二乘算法得到行驶阻力及质量的估计值.在电动轮驱动电动汽车平台上分别进行了沥青、塑胶及碎石路面上以及坡道上的试验,分析了行驶阻力与质量辨识的误差与收敛情况,并针对几种特殊工况对算法进行适应性改进.试验结果显示,不同质量及道路状态下的估计误差均在2.5%以下,表明所设计的辨识算法具有很高的估计精度,具有良好的工程应用价值.

A new algorithm for vehicle mass estimation was studied based on the on-road test of an in-wheel motor vehicle.Containing the road gradient information in the longitudinal accelerometer signal,the algorithm removed the road grade from the longitudinal dynamics of the vehicle.Then,two different recursive least squares（RLS） schemes were proposed to estimate the driving resistance and the mass independently based on the acceleration partition.Experiments on the asphalt road,the plastic runway,and the gravel road as well as experiments with road grade were carried out.The estimation errors and the result convergence were analyzed.Then,according to several critical operating conditions,the adaptability of the algorithm was improved.The experimental data show that the estimation error is within 2.5% with various masses and different roads,which indicates that the algorithm can accurately estimate mass and its engineering application is valuable.