Literature DB >> 34073574

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.

Wenfei Li1,2,3, Huiyun Li1,2,3, Kun Xu1,2,3, Zhejun Huang1,2,3, Ke Li1,2,3, Haiping Du4.   

Abstract

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

Entities:  

Keywords:  Unscented Kalman Filter; multiple-model; vehicle dynamic parameters

Year:  2021        PMID: 34073574     DOI: 10.3390/s21113711

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

1.  Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

Authors:  Leandro Vargas-Melendez; Beatriz L Boada; Maria Jesus L Boada; Antonio Gauchia; Vicente Diaz
Journal:  Sensors (Basel)       Date:  2017-04-29       Impact factor: 3.576

2.  Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs.

Authors:  José Luis Torres-Moreno; José Luis Blanco-Claraco; Antonio Giménez-Fernández; Emilio Sanjurjo; Miguel Ángel Naya
Journal:  Sensors (Basel)       Date:  2016-03-04       Impact factor: 3.576

Review 3.  Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey.

Authors:  Xianjian Jin; Guodong Yin; Nan Chen
Journal:  Sensors (Basel)       Date:  2019-10-03       Impact factor: 3.576

  3 in total
  2 in total

1.  A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving.

Authors:  Lixin Yan; Jiayu Chen; Chengyue Wen; Ping Wan; Liqun Peng; Xujin Yu
Journal:  Comput Intell Neurosci       Date:  2022-04-15

2.  Advanced Sensing and Control for Connected and Automated Vehicles.

Authors:  Chao Huang; Haiping Du; Wanzhong Zhao; Yifan Zhao; Fuwu Yan; Chen Lv
Journal:  Sensors (Basel)       Date:  2022-02-16       Impact factor: 3.576

  2 in total

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