摘要
为提高车辆状态估计结果的精度与可靠性,设计了一种车辆纵向力和质心侧偏角层级估计方法。研究了一种用于车辆行驶状态估计的加权容积卡尔曼滤波,采用移动窗估计法调整测量噪声的协方差矩阵,根据不同时刻信息对测量噪声统计的有用性,动态调整窗口中不同时刻信息的权重,从而提高车辆状态观测器的滤波精度。根据电驱动轮模型特点,并考虑轮胎松弛长度来构造纵向力微分方程,从而设计了纵向力观测器。在纵向力估计的基础上,将上层的纵向力估计值视为伪量测值,利用三自由度车辆动力学模型设计了基于级联卡尔曼滤波的车辆行驶状态估计策略,实现了车辆质心侧偏角估计。进行了变速正弦转向工况和定速Fishhook转向工况下的CarSim/Simulink联合仿真试验以及实车试验,结果表明:所提方法整体估计精度相比扩展卡尔曼滤波提升了6.82%,具有较高的估计精度和实时跟踪效果,满足车辆应用需求。
To improve the accuracy and reliability of vehicle driving state estimation,a hierarchical estimation method of vehicle longitudinal force and sideslip angle is proposed.A weighted cubature Kalman filter for vehicle driving state estimation is designed,where the covariance matrix of measurement noise is adjusted with the moving window estimation method.And according to the usefulness of the information at different sampling moment to the measurement noise statistics,the weight of information is dynamically adjusted to improve the filtering accuracy of vehicle state observer.Following the characteristics analysis for the electric drive wheel model and considering the tire relaxation length,the longitudinal force differential equation is constructed,and the longitudinal force observer is designed.According to the longitudinal force estimation,the upper longitudinal force estimation is regarded as the pseudo measurement,and a vehicle state estimation strategy based on cascaded Kalman filter is designed via three-degree-of-freedom vehicle dynamics model to achieve the estimation of vehicle sideslip angle.Joint simulation test in CarSim/Simulink under variable-speed sinusoidal-steering manoeuvre condition and fixed-speed fishhook-steering manoeuvre condition,as well as real vehicle test,is carried out.The results show that the proposed estimation method has high estimation accuracy and real-time tracking effect,and the overall estimation accuracy is improved by 6.82%compared with that of the extended Kalman filter.
作者
陈特
蔡英凤
陈龙
徐兴
江浩斌
孙晓强
CHEN Te;CAI Yingfeng;CHEN Long;XU Xing;JIANG Haobin;SUN Xiaoqiang(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;Automotive Engineering Research Institute,Jiangsu University,Zhenjiang,Jiangsu 212013,China)
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2019年第11期131-140,147,共11页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金重点资助项目(U1664258,U1564201)
国家重点研发计划资助项目(2017YFB0102603)
关键词
车辆状态估计
纵向力
容积卡尔曼滤波
层级估计方法
vehicle state estimation
longitudinal force
cubature Kalman filter
hierarchical estimation
