摘要
针对车辆在行驶过程中难以实时、准确地获取路面附着系数这一问题,本研究在结合车辆三自由度动力学模型和Dugoff修正轮胎力模型所搭建的四毂驱动联合仿真电动汽车平台基础上,设计了一种时效性、鲁棒性强的双容积卡尔曼滤波路面附着系数观测算法。双容积卡尔曼滤波算法利用奇异值分解优化求解误差协方差矩阵,将车辆行驶状态观测器信息与附着系数观测器信息相互联系,形成闭环反馈校正更新观测信号,实现对路面附着系数的实时估计。在四轮毂驱动联合仿真电动汽车平台中设置低附着路面,在开路面仿真工况下对双容积卡尔曼滤波算法进行验证,并与传统容积卡尔曼滤波观测器数据进行比较和分析。结果表明:双容积卡尔曼滤波算法具有更快的的响应速度,估计的路面附着系数精度更高,实时性更强。
It is difficult to obtain the road surface adhesion coefficient in real time and accurately during the running of the vehicle.Aiming at this problem,a four-hub drive co-simulation electric vehicle platform combined with a three-degree-of-freedom dynamic model and a Dugoff modified tire force model was built, and a double cubature Kalman observer with strong timeliness and robustness for road adhesion coefficient was designed. The observer algorithm uses the singular value decomposition to solve the error covariance matrix, which can update the vehicle speed information in real time, and correlate the vehicle driving state observer information with the adhesion coefficient observer cycle to form a closed-loop feedback correction to realize real-time estimation of the road surface adhesion coefficient. The low-attachment road surface and bisectional road surface condition was setted in the four-wheel hub drive co-simulation electric vehicle platform to verify double cubature Kalman filter algorithm and compared with the cubature Kalman filter observer data.The results show that the algorithm has faster response speed, higher accuracy and better real-time performance in estimating road adhesion coefficient.
作者
刘志强
刘逸群
LIU Zhi-qiang;LIU Yi-qun(School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha,410114,China)
出处
《长沙理工大学学报（自然科学版）》
CAS
2019年第3期55-62,共8页
Journal of Changsha University of Science and Technology:Natural Science
基金
国家自然科学基金资助项目(11572055)
