Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering

Changfu Zong, Pan Song, Dan Hu

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients. The estimator is designed based on a vehicle model with three degrees of freedom (3-DOF) and the dual extended Kalman filter (DEKF) technique is employed. Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions (high-friction, low-friction, and joint-friction roads). Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states (e.g., yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.
Original languageEnglish
Pages (from-to)446-452
Number of pages7
JournalJournal of Zhejiang University Science A (Applied Physics & Engineering)
Volume12
Issue number6
DOIs
Publication statusPublished - 1 Jun 2011

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Tires
Friction
Extended Kalman filters
Adhesion

Keywords

  • Vehicle dynamics
  • State estimation and system identification
  • Active safety and passive safety

Cite this

Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering. / Zong, Changfu; Song, Pan; Hu, Dan.

In: Journal of Zhejiang University Science A (Applied Physics & Engineering), Vol. 12, No. 6, 01.06.2011, p. 446-452.

Research output: Contribution to journalArticle

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