基于稳态卡尔曼滤波的车辆质量与道路坡度估计*

doi:10.19562/j.chinasae.qcgc.2018.09.009

汽车工程 ›› 2018, Vol. 40 ›› Issue (9): 1062-1067.doi: 10.19562/j.chinasae.qcgc.2018.09.009

基于稳态卡尔曼滤波的车辆质量与道路坡度估计^*

郝胜强,罗培培,席军强

北京理工大学机械与车辆学院,北京 100081

收稿日期:2017-09-04 出版日期:2018-09-25 发布日期:2018-09-25
通讯作者: 席军强,教授,博士生导师,E-mail:xijunqiang@bit.edu.cn
基金资助:
国家自然科学基金(51375053)资助

Estimation of Vehicle Mass and Road Slope Based on Steady-state Kalman Filter

Hao Shengqiang, Luo Peipei & Xi Junqiang

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081

Received:2017-09-04 Online:2018-09-25 Published:2018-09-25

摘要/Abstract

摘要： 针对车辆自动变速器控制系统难以实时测得车辆质量与道路坡度这一问题,搭建了车辆的纵向运动学和动力学模型,在理论模型的基础上使用离散卡尔曼滤波器对车辆质量和道路坡度进行估算,并利用Carsim与Maltab/Simulink联合仿真采用合适的加速度传感器和稳态卡尔曼滤波器的实车试验,验证了用此方法估算车辆质量和道路坡度比惯性导航仪得到的数据有更好的实时性和准确性。

关键词: 道路坡度, 车辆质量, 卡尔曼滤波器, Carsim/Simulink联合仿真, 加速度传感器

Abstract: In view of that the automatic transmission control system of vehicle is difficult to measure the vehicle mass and road slope, the longitudinal kinematic and dynamic models of vehicle are built, based on which discrete Kalman filter is used to estimate vehicle mass and road slope. The co-simulation with Carsim and Maltab/Simulink and the real vehicle test with proper acceleration sensor and steady-state Kalman filter verify that using the method proposed to estimate vehicle mass and road slope has better real-time performance and accuracy than those obtained from inertial navigator

Key words: road slope, vehicle mass, steady-state Kalman filter, Carsim/Simulink co-simulation, acceleration sensor

郝胜强,罗培培,席军强. 基于稳态卡尔曼滤波的车辆质量与道路坡度估计^*[J]. 汽车工程, 2018, 40(9): 1062-1067.

Hao Shengqiang, Luo Peipei & Xi Junqiang. Estimation of Vehicle Mass and Road Slope Based on Steady-state Kalman Filter[J]. , 2018, 40(9): 1062-1067.

参考文献

[1] 谷文豪,王洪亮,皮大伟,等.基于倾角传感器的坡道角度识别研究[J] .河北科技大学学报,2015,36(6):566-572.
[2] 高婷婷,张海,刘倩,等.一种低成本动态车载导航坡度识别方法[J] .系统仿真技术,2009,5(4):28-33.
[3] BAE H S, RYU J, GERDES C. Road grade and vehicle parameter estimation for longitudinal control using GPS[C] . IEEE Intelligent Transportation Systems Proceedings,2001.
[4] SCHROEDL S, WAGSTAFF K, ROGERS S, et al. Mining GPS traces for map refinement[J] . Data Mining and Knowledge Discovery,2004(9):59-87.
[5] 金辉,李磊,李斌虎,等.基于加速度区间判断的坡道识别方法[J] .中国公路学报,2010(1):122-126.
[6] 杨殿阁,连小珉,张涛,等.基于低成本加速度传感器的车载道路坡度识别装置及方法:101509768[P] .2009-08-19.
[7] 刘洪波,雷雨龙,傅尧,等.基于转矩的车辆负载识别方法[J] .吉林大学学报(工学版),2012,42(5):1107-1112.
[8] MAHYUDDIN M N, NA J, HERRMANN G, et al. An adaptive observer-based parameter estimation algorithm with application to road gradient and vehicle's mass estimation[C] . UKACC International Conference on Control, Cardiff UK,3-5 September 2012:102-107.
[9] 金辉,葛安林,秦贵和,等.基于纵向动力学的坡道识别方法研究[J] .机械工程学报,2002,38(1):79-86.
[10] HUANG X, WANG J. Longitudinal motion based lightweight vehicle payload parameter real-time estimations[J] . Journal of Dynamic Systems, Measurement, and Control,2013(1):011013/1- 011013/10.
[11] FATHY H K, KANG D, STEIN J L. Online vehicle mass estimation using recursive least squares and supervisory data extraction[C] . American Control Conference, IEEE,2008:1842-1848.
[12] 晋磊.AMT重型越野车辆换挡规律优化[D] .北京:北京理工大学,2015.
[13] 程鸣.自动变速车辆坡道行驶协同控制技术研究[D] .北京:北京理工大学,2012.
[14] 雷雨龙,付尧,刘科,等.基于扩展卡尔曼滤波的车辆质量与道路坡度估计[J] .农业机械学报,2014,45(11):9-13.

基于稳态卡尔曼滤波的车辆质量与道路坡度估计^*

Estimation of Vehicle Mass and Road Slope Based on Steady-state Kalman Filter

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