卡尔曼滤波方法估计车辆质量与道路坡度对比分析  

Comparative analysis on vehicle mass and road slope with the Kalman Filter approach

作　　者：苏庆列[1] 黄鹏 SU Qing-lie;HUANG Peng(Automobile School,Fujian Chuanzheng Communications College,Fuzhou 350007;Automotive Engineering Institute,Guangzhou Automobile Group Co.,Ltd.,Guangzhou 511434)

机构地区：[1]福建船政交通职业学院汽车学院,福建福州350007 [2]广州汽车集团股份有限公司汽车工程研究院,广东广州511434

出　　处：《机械设计》2021年第7期105-109,共5页Journal of Machine Design

基　　金：福建省交通厅科技项目(201935)。

摘　　要：针对传统车辆控制系统难以实时准确测量整车质量和道路坡度的问题,建立车辆纵向动力学方程,采用欧拉前向方法将系统方程进行离散化,运用扩展卡尔曼滤波算法和无迹卡尔曼滤波算法,分别设计了整车质量观测器、道路坡度观测器、整车质量和道路坡度联合观测器,并进行了对比分析。在Simulink/Carsim环境下进行了联合仿真分析,采用Carsim输出数据代替实车数据对观测器进行了验证。结果表明,两种算法都能够有效地估计出车辆的整车质量和道路坡度,但与扩展卡尔曼滤波对整车质量估计结果相比,无迹卡尔曼滤波算法具有较小波动、收敛速度快的优点。In this article,since vehicle mass and road slop are difficult to be measured by means of the conventional control system,the model of longitudinal dynamics is set up,and the model is discretized with the aid of the Forward Euler method. The vehicle-mass observer,the road-slop observer,and the joint observer of vehicle mass and road slop are designed based on the algorithms of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF),so as to estimate vehicle mass and road slop,and the results are compared respectively. The simulated analysis is carried out in the Simulink/Carsim environment. The Carsim software is used for data output,and the data takes place of the real-vehicle data,in order to verify the effectiveness of these observers. The results show that thanks to these algorithms,vehicle mass and road slop are estimated effectively. Compared with the algorithm of Extended Kalman Filter (EKF),the algorithm of Unscented Kalman Filter (UKF) has more desirable results with such characteristics as minor fluctuation and fast convergence speed.

关 键 词：整车质量 道路坡度 欧拉前向方法 卡尔曼滤波 Simulink/Carsim 

分 类 号：U462.3[机械工程—车辆工程]