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

被引：0
作者：
Zhang Z. [1 ]
Liu C. [1 ]
Ma X. [1 ]
Zhang Y. [1 ]
机构：
[1] Department of Weapons and Control Engineering, Army Academy of Armored Forces, Beijing
来源：
Binggong Xuebao/Acta Armamentarii | 2020年 / 41卷 / 05期
关键词：
All-wheel independent drive electric vehicle; Data fusion; Mass center sideslip angle; Unscented Kalman filter;
D O I：
10.3969/j.issn.1000-1093.2020.05.002
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Board of Acta Armamentarii. All right reserved.
引用
收藏
页码：842 / 849
页数：7
相关论文
共 15 条
  • [1] YU Z P, GAO X J., Review of vehicle state estimation problem under driving situation, Chinese Journal of Mechanical Engineering, 45, 5, pp. 20-33, (2009)
  • [2] CHEN H, GAO B L, XU F., Review on vehicle sideslip angle estimation, Chinese Journal of Mechanical Engineering, 49, 24, pp. 76-94, (2013)
  • [3] KIENCKE U, DAISS A., Observation of lateral vehicle dynamics, Control Engineering Practice, 5, 8, pp. 1145-1150, (1997)
  • [4] DAILY R, BEVLY D., The use of GPS for vehicle stability control system, IEEE Transactions on Industrial Electronics, 51, 2, pp. 270-277, (2004)
  • [5] LIU F, XIONG L, WU X P, Et al., Vehicle sideslip angle estimation and contrastive analysis, Journal of Tongji University(Natural Science), 43, 3, pp. 448-455, (2015)
  • [6] CHEN T, CAI Y F, CHEN L, Et al., Design and verification of vehicle longitudinal force and sideslip angle hierarchical estimation method, Journal of Xi'an Jiaotong University, 53, 11, pp. 2-7, (2019)
  • [7] BONYAN K, GHORBANI S, JANABI F., Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles, Aerospace Science and Technology, 92, 6, pp. 446-463, (2019)
  • [8] WANG Z F, LIU M C, ZHOU Y., Estimation of longitudinal speed of in-wheel motor driven vehicle using fuzzy extended Kalman filter, Journal of Xi'an Jiaotong University, 50, 6, pp. 1094-1099, (2015)
  • [9] 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, Journal of Jilin University (Engineering and Technology Edition), 48, 3, pp. 845-851, (2018)
  • [10] SUN C Y, ZHANG X, JIN B., Vehicle body side slip angle estimation based on genetic particle filter for in-wheel motored electric vehicle, Journal of Beijing Jiaotong University, 40, 6, pp. 102-106, (2016)
12