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CLC number: U461.1

On-line Access: 2011-06-07

Received: 2011-02-25

Revision Accepted: 2011-04-23

Crosschecked: 2011-05-24

Cited: 5

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Journal of Zhejiang University SCIENCE A 2011 Vol.12 No.6 P.446-452

http://doi.org/10.1631/jzus.A1100056


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


Author(s):  Chang-fu Zong, Pan Song, Dan Hu

Affiliation(s):  State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China

Corresponding email(s):   cfzong@yahoo.com.cn

Key Words:  Vehicle dynamics, State estimation and system identification, Active safety and passive safety


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.

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author="Chang-fu Zong, Pan Song, Dan Hu",
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%T Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering
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%I 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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SP - 446
EP - 452
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PB - Zhejiang University Press & Springer
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bakker, E., Pacejka, H., 1989. A New Tire Model with an Application in Vehicle Dynamics Studies. Autotechnologies Conference and Exposition, Monte-Carlo, Monaco.

[2]Best, M.C., Gordon, T.J., 2000. Combined State and Parameter Estimation of Vehicle Handling Dynamics. Proceedings of 5th International Symposium on Advanced Vehicle Control, Ann Arbor, MI, USA.

[3]Guo, K., 1991. Vehicle Handling Dynamics. Jilin Science & Technology Press, Changchun, China (in Chinese).

[4]Ray, L.R., 1995. Nonlinear state and tire force estimation for advanced vehicle control. IEEE Transaction on Control Systems Technology, 3(1):117-124.

[5]Welch, G., Bishop, G., 1995. An Introduction to the Kalman Filter. Technical Report TR 95-041, Chapel Hill, NC, USA.

[6]Wenzel, T.A., Burnham, K.J., Blundell, M.V., Williams, R.A., 2006. Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics, 44(2):153-171.

[7]Yu, Z.P., Gao, X.J., 2009. Review of vehicle state estimation problem under driving situation. Chinese Journal of Mechanical Engineering, 45(5):20-33 (in Chinese).

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