基于IMM的重型商用车状态参数和道路坡度联合估计方法
Joint Estimation Method of State Parameters and Slope of Heavy Commercial Vehicles Based on Interactive Multiple Models
刘刚 1王豪豪 2乔林炎1
作者信息
- 1. 河南工学院 车辆与交通工程学院,河南 新乡 453003
- 2. 湖北汽车工业学院 汽车工程学院,湖北 十堰 442002
- 折叠
摘要
实时地估计重型商用车状态参数和行驶工况,有助于车辆的稳定性控制和驱动模式的实时切换.文中提出了一种重型商用车状态参数联合估计方法,建立了车辆运动学模型和动力学模型,将IMM算法和SRCKF算法融合,实现了对车辆行驶道路坡度的估计.利用滑模观测器估计车辆轮胎力,并在获得轮胎纵向力的基础上,利用查表反馈的方法估计路面附着系数.最后,利用硬件在环仿真台架对联合估计方法进行了验证,结果表明该算法具有较好的精度和实时性.
Abstract
In order to accurately and real-time estimate the state parameters and driving conditions of heavy commercial vehicles,so as to facilitate the stability control and driving mode real-time switching of heavy commercial vehicles.In this paper,the kinematics model and dynamics model of commercial vehicles are established.The multi-model interaction(IMM)algorithm and the square root volume Kalman filter algorithm are combined to estimate the road slope of heavy commercial vehicles.The sliding mode observation is used to estimate the vehicle tire force.On the basis of obtaining the tire longitudinal force,the road adhesion coefficient is estimated by using the method of look-up feedback.Finally,the joint estimation method is verified by the hardware-in-the-loop test bench,and the verification results show that the algorithm has good accuracy and real-time performance.
关键词
车辆动力学/道路坡度/IMM算法/SRCKF算法/硬件在环测试Key words
vehicle dynamics/road slope/interactive multiple models/square root cubature kalman filter/hardware in the Loop引用本文复制引用
出版年
2024
NETL
NSTL
万方数据