Estimation of vehicle mass and road slope based on steady-state Kalman filter

Shengqiang Hao, Peipei Luo, Junqiang Xi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.

Original languageEnglish
Title of host publicationProceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
EditorsXin Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages582-587
Number of pages6
ISBN (Electronic)9781538631065
DOIs
Publication statusPublished - 2 Jul 2017
Event2017 IEEE International Conference on Unmanned Systems, ICUS 2017 - Beijing, China
Duration: 27 Oct 201729 Oct 2017

Publication series

NameProceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Volume2018-January

Conference

Conference2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Country/TerritoryChina
CityBeijing
Period27/10/1729/10/17

Keywords

  • Acceleration sensor
  • Carsim/Matlab Co-simulation
  • Discrete Steady-state Kalman filter
  • Recognition of the Road slope and Vehicle Mass

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