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

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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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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Chang-fu Zong, Pan Song & Dan Hu

Authors
  1. Chang-fu Zong
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  2. Pan Song
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  3. Dan Hu
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Corresponding author

Correspondence to Chang-fu Zong.

Additional information

Project (Nos. 50775096 and 51075176) supported by the National Natural Science Foundation of China

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Zong, Cf., Song, P. & Hu, D. Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering. J. Zhejiang Univ. Sci. A 12, 446–452 (2011). https://doi.org/10.1631/jzus.A1100056

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  • DOI: https://doi.org/10.1631/jzus.A1100056

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