一种基于数据融合的全轮驱动车辆质心侧偏角估计方法

张征;刘春光;马晓军;张运银

兵工学报 ›› 2020, Vol. 41 ›› Issue (5) : 842-849.

EN 投稿 期刊 文章 图表 检索
高级检索

  • 主管：中国科学技术协会

    主编：许毅达

    ISSN 1000-1093

    　

  • 主办：中国兵工学会

    出版：《兵工学报》 编辑部

    CN 11-2176/TJ

《兵工学报》是我国国防科技领域最具影响力的学术期刊之一，创刊于1979年，目前被国内所有重要数据库收录，其综合评价指标近年来一直名列兵工技术类科技期刊的首位；《兵工学报》目前还被国际上著名的检索机构美国......
兵工学报 ›› 2020, Vol. 41 ›› Issue (5) : 842-849. DOI: 10.3969/j.issn.1000-1093.2020.05.002
论文

一种基于数据融合的全轮驱动车辆质心侧偏角估计方法

  • 张征, 刘春光, 马晓军, 张运银
作者信息 +

Method for Estimating Sideslip Angle of All-wheel Drive Vehicle Based on Data Fusion

  • ZHANG Zheng， LIU Chunguang， MA Xiaojun， ZHANG Yunyin
Author information +
文章历史 +

摘要

为准确估计全轮电驱动车辆行驶状态参数，设计了一种基于数据融合的质心侧偏角估计方法。该方法充分利用低成本普通车载传感器信息、电机输入信息和驾驶信号，在建立非线性3自由度 车辆模型和轮胎模型基础上，采用无迹卡尔曼滤波算法对质心侧偏角进行估计；同时通过信号积分法估计质心侧偏角，结合车辆行驶工况和路面条件，将无迹卡尔曼滤波和信号积分两种算法结果进行了数据融合。基于硬件在环实时仿真平台进行了车辆操纵仿真验证，结果表明，提出的估计算法与单一估计算法相比，具有更高的观测精度，能够满足多种行驶工况下的质心侧偏角观测需求。

Abstract

A method for estimating sideslip angle based on data fusion is proposed to obtain the driving state parameters of all-wheel electric drive vehicle. On the basis of three-degree-of-freedom vehicle model and tire model, the method fully utilizes the information from a low-cost common vehicle-mounted sensor, in-wheel motor input information as well as driving signals, and the sideslip angle is estimated by using the unscented Kalman filter algorithm. Besides, the sideslip angle is estimated by signal integration method. Combined with vehicle driving conditions and road conditions, the estimated values of the unscented Kalman filtering algorithm and the signal integral algorithm are fused to obtain the final estimation of sideslip angle. A series of simulations were conducted on the hardware-in-the-loop real-time simulation platform. The results show that the proposed estimation algorithm has higher observation accuracy compared with the single estimation algorithm, which can meet the requirements of mass center sideslip angle observation under various driving conditions. Key

关键词

全轮独立电驱动车辆 / 数据融合 / 质心侧偏角 / 无迹卡尔曼滤波

Key words

all-wheelindependentdriveelectricvehicle / datafusion / masscentersideslipangle / unscentedKalmanfilter

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
张征, 刘春光, 马晓军, 张运银. 一种基于数据融合的全轮驱动车辆质心侧偏角估计方法. 兵工学报. 2020, 41(5): 842-849 https://doi.org/10.3969/j.issn.1000-1093.2020.05.002
ZHANG Zheng， LIU Chunguang， MA Xiaojun， ZHANG Yunyin. Method for Estimating Sideslip Angle of All-wheel Drive Vehicle Based on Data Fusion. Acta Armamentarii. 2020, 41(5): 842-849 https://doi.org/10.3969/j.issn.1000-1093.2020.05.002

基金

武器装备预先研究项目（301051102）

参考文献



［1］余卓平, 高晓杰. 车辆行驶过程中的状态估计问题综述［J］. 机械工程学报,2009, 45(5): 20-33.
YU Z P, GAO X J. Review of vehicle state estimation problem under driving situation［J］. Chinese Journal of Mechanical Engineering, 2009, 45(5): 20-33.(in Chinese)
［2］陈慧, 高博麟, 徐帆. 车辆质心侧偏角估计综述［J］. 机械工程学报, 2013, 49(24):76-94.
CHEN H, GAO B L, XU F. Review on vehicle sideslip angle estimation［J］. Chinese Journal of Mechanical Engineering, 2013,49(24):76-94.(in Chinese)
［3］KIENCKEU, DAISS A. Observation of lateral vehicle dynamics［J］. Control Engineering Practice, 1997, 5(8): 1145-1150.
［4］DAILY R, BEVLY D. The use of GPS for vehicle stability control system［J］. IEEE Transactions on Industrial Electronics, 2004, 51(2): 270-277.
［5］刘飞, 熊璐, 邬肖鹏, 等. 车辆质心侧偏角估计算法设计与对比分析［J］. 同济大学学报(自然科学版), 2015, 43(3): 448-455.
LIU F, XIONG L, WU X P, et al. Vehicle sideslip angle estimation and contrastive analysis［J］. Journal of Tongji University(Natural Science), 2015, 43(3): 448-455.(in Chinese)
［6］陈特, 蔡英凤, 陈龙, 等. 车辆纵向力和质心侧偏角层级估计方法设计与验证［J］. 西安交通大学学报, 2019, 53(11): 2-7.
CHEN T, CAI Y F, CHEN L, et al. Design and verification of vehicle longitudinal force and sideslip angle hierarchical estimation method［J］. Journal of Xi'an Jiaotong University, 2019, 53(11): 2-7.(in Chinese)
［7］BONYAN K, GHORBANI S, JANABI F. Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles［J］. Aerospace Science and Technology, 2019, 92(6): 446-463.
［8］王志福, 刘明春, 周杨. 基于模糊扩展卡尔曼滤波的轮毂电机驱动车辆纵向速度估计算法［J］. 西安交通大学学报, 2015, 50(6): 1094-1099.
WANG Z F, LIU M C, ZHOU Y. Estimation of longitudinal speed of in-wheel motor driven vehicle using fuzzy extended Kalman filter［J］. Journal of Xi'an Jiaotong University,2015, 50(6): 1094-1099.(in Chinese)
［9］田彦涛, 张宇, 王晓玉, 等. 基于平方根无迹卡尔曼滤波算法的电动汽车质心侧偏角估计［J］. 吉林大学学报(工学版), 2018, 48(3): 845-851.
TIAN Y T, ZHANG Y, WANG X Y, et al. Estimation of side-slip angle of electric vehicle based on square-root unscented Kalman filter algorithm［J］. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(3): 845-851.(in Chinese)

［10］孙传扬, 张欣, 靳彪. 基于遗传粒子滤波的轮毂驱动电动汽车质心侧偏角估计方法［J］. 北京交通大学学报, 2016, 40(6):102-106.
SUN C Y, ZHANG X, JIN B. Vehicle body side slip angle estimation based on genetic particle filter for in-wheel motored electric vehicle［J］. Journal of Beijing Jiaotong University, 2016, 40(6):102-106.(in Chinese)
［11］李刚, 王野, 宗长富. 四轮轮毂电机电动汽车行驶状态估计［J］. 汽车工程, 2018, 40(2): 150-155.
LI G, WANG Y, ZONG C F. Driving state estimation of electric vehicle with four-wheel-hub-motors ［J］. Automotive Engineering, 2018, 40(2): 150-155.(in Chinese)
［12］刘明春. 8×8轮毂电机驱动车辆操纵稳定性分析与控制研究［D］. 北京: 北京理工大学, 2015.
LIU M C. Study on handling stability analysis and control for 8×8 in-wheel motor drive vehicle［D］. Beijing: Beijing Institute of Technology, 2015.(in Chinese)
［13］MOUSTAPHAD, ALESSANDRO C. Onboard real-time estimation of vehicle lateral tire-road forces and sideslip angle［J］. ASME Transactions on Mechatronics, 2011, 16(4): 601-613.
［14］阳贵兵, 李长兵，廖自力, 等. 一种全工况适应的全轮独立驱动车辆车速估计方法［J］. 兵工学报, 2015, 36(10): 2006-2011.
YANG G B, LI C B, LIAO Z L, et al. Method of vehicle speed estimation adapted in all conditions for all-wheel independent drive vehicle ［J］. Acta Armamentarii, 2015, 36(10): 2006-2011.（in Chinese）
［15］CHEN T, XU X, CHEN L. Estimation of longitudinal force, lateralvehicle speed and yaw rate for four-wheel independent driven electric vehicles［J］. Mechanical Systems and Signal Processing, 2018, 101(3): 377-388.





第41卷第5期2020年5月
兵工学报ACTA ARMAMENTARII
Vol.41No.5May2020

文章所在专题

特种车辆理论与技术

592

Accesses

0

Citation

Detail
段落导航
相关文章

/

〈 〉