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

Changfu Zong, Pan Song, Dan Hu

    Research output: Contribution to journalArticlepeer-review

    23 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

    Keywords

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

    Fingerprint

    Dive into the research topics of 'Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering'. Together they form a unique fingerprint.

    Cite this