摘要
汽车质量和道路坡度是设计高性能汽车决策和控制算法所需的基本参数。但是,微小加速度行驶工况下,汽车纵向惯性力较小,汽车质量和道路坡度难解耦,且现有估计算法的准确性和收敛速度有待进一步提高。该文提出了一种基于车辆纵向动力学原理的自适应无迹Kalman滤波(adaptive unscented Kalman filter, AUKF)算法,通过加大质量预测模型初始噪声,并设计自适应收缩系数对预测误差协方差矩阵进行动态调整,实现了微小加速度工况下汽车质量和道路坡度的快速准确联合估计。其中,自适应收缩系数与质量预测模型噪声和质量估计值有关。不同初始条件下的仿真与实车试验结果表明,AUKF算法能准确估计汽车质量和道路坡度,且质量估计误差均小于3%,道路坡度估计均方根误差均小于0.4°。此外,在纵向加速度小于0.3 m/s;行驶工况下,相比经典UKF算法,AUKF算法极大加快了汽车质量和道路坡度估计的收敛速度,在质量初始误差小于40%条件下,质量误差收敛至3%以内只需约10 s。
Vehicle mass and the road grade are key input parameters for automated vehicle decision and control algorithms. These two parameters are difficult to know accurately, but are tightly coupled for small acceleration driving scenarios with small inertias. The accuracy and convergence speed of existing estimation algorithms need to be further improved. A vehicle dynamics model is used here with an adaptive unscented Kalman filter(AUKF) algorithm for rapid, accurate joint estimates of the vehicle mass and road grade during small acceleration scenarios. The prediction model has a large initial mass noise with an adaptive shrink coefficient designed to adjust the prediction error covariance matrices. The adaptive shrink coefficient is related to the estimation result and the prediction error covariance matrices. Simulations and vehicle tests for various initial conditions show that the AUKF algorithm can accurately estimate the vehicle mass and road grade with an estimated vehicle mass error of less than 3% and a root-mean-square error in the road grade of less than 0.4°. In addition, the AUKF algorithm gives better convergence speeds for the vehicle mass and road grade estimates with longitudinal accelerations less than 0.3 m/s;than the traditional UKF algorithm. When the initial mass error is within 40%, the estimated mass error converges to less than 3% within 10 seconds.
作者
孙恩鑫
殷玉明
辛喆
李升波
何举刚
孔周维
刘秀鹏
SUN Enxin;YIN Yuming;XIN Zhe;LI Shengbo;HE Jugang;KONG Zhouwei;LIU Xiupeng(College of Engineering,China Agricultural University,Beijing 100083,China;College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China;Department of Automotive Engineering,Tsinghua University,Beijing 100084,China;Chongqing Chang'an Automobile Co.,Ltd.,Chongqing 400023,China)
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第1期125-132,共8页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金青年科学基金项目(51905483)
国家国际科技合作项目(2019YFE0100200)。
