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CLC number: U461.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-05-24
Cited: 5
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Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering
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Chang-fu Zong, Pan Song, Dan Hu. Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering[J]. Journal of Zhejiang University Science A, 2011, 12(6): 446-452.
@article{title="Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering",
author="Chang-fu Zong, Pan Song, Dan Hu",
journal="Journal of Zhejiang University Science A",
volume="12",
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pages="446-452",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1100056"
}
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T1 - Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering
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DOI - 10.1631/jzus.A1100056
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.
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