摘要提高汽车稳定性是一个重要的车辆安全性指标，对此，车辆行驶状态参数的获取有着非常重要的意义。由于技术、噪声、成本等问题，很多汽车状态参数不能直接，便捷，有效的量测出。为此，通过对软测量技术原理的学习，基于卡尔曼滤波算法，分别对汽车2自由度模型和3自由度模型进行行驶状态估计。本文采取了一般SUV汽车来进行数学建模；利用CarSim软件中的E-class SUV车辆模型进行双移线行驶路线实验的动力学仿真，得到有关仿真曲线及对应实验数据。接下来利用MATLAB编程进行卡尔曼滤波和扩展卡尔曼滤波（EKF）算法编程仿真，将CarSim的输入和观测信号进行滤波验证，并对其进行了输出值的比较。最后得出结论，EKF算法可满足车辆质心3自由度行驶状态估计的要求。84690

毕业论文关键词  软测量技术  车辆行驶状态  卡尔曼滤波   扩展卡尔曼滤波（EKF） 

Matlab    CarSim    2自由度动力学模型  3自由度动力学模型

毕业设计说明书外文摘要

Title      Estimation of Vehicle State Information based on Soft-Sensing Techique                 

Abstract It is of great importance to obtain vehicle state so as to enhance vehicle stability。 However, since some problems, such as the limit of technology, cost and noise, a number of car status parameters cannot be measured directly, effectively and easily, which has caused great difficulties for vehicle safety control。 Thus, this paper learned about the theory of soft sensing, through which built the 2-degrees-of-freedom model and 3-degrees-of-freedom model for vehicle based on the Kalman Filter。 The vehicle model used for simulation is an ordinary kind of SUV from CarSim E-class SUV, while in open loop test and verify。 Then, use the data from CarSim by Double Lane Change path test (a kind of closed-loop driver controls model) to simulate in MATLAB using KF and EKF。 In the MATLAB environment, use the input signal and the observed signal from CarSim to validate filtering algorithm, and compare with actual output value from CarSim and the filtered estimates。 Finally, EKF algorithm satisfies the requirements of the 3-degrees-of-freedom model for vehicle state。 

Keywords  Soft-Sensing Technique    vehicle state      Kalman Filter 

 EKF  MATLAB   CarSim  2-degrees-of-freedom model  3-degrees-of-freedom model

目   次

目   次 I

1  引言 1

1。1  课题背景 1

1。2  车辆行驶状态估计 2

1。3  软测量技术 3

1。3。1  软测量技术概念 3

1。3。2  软测量技术基本原理 3

1。3。3  软测量技术的应用 4

1。3。4  软测量技术研究现状 5

1。4  本文研究方法 6

2  CarSim实车仿真试验 7

2。1  CarSim软件概述 7

2。2  两种工况下的车辆动力学试验 8

2。2。1  双移线试验 8

2。2。2  角阶跃试验 13

2。3  本章小结