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JRM Vol.30 No.5 pp. 811-818
doi: 10.20965/jrm.2018.p0811
(2018)

Paper:

Proposal of the Tire Longitudinal Characteristics Real-Time Estimation Method

Yuuki Shiozawa, Shunsuke Tsukuda, and Hiroshi Mouri

Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Kognei-shi, Tokyo 184-8588, Japan

Received:
May 20, 2018
Accepted:
September 1, 2018
Published:
October 20, 2018
Keywords:
vehicle dynamics, vehicle stability, road friction coefficient, tire slip ratio, tire stiffness
Abstract
Proposal of the Tire Longitudinal Characteristics Real-Time Estimation Method

The tire-road friction coefficient estimator

For vehicle dynamics control and Autonomous Driving (AD) system, it is important to know the friction coefficient μ of the road surface accurately. It is because the lateral and the longitudinal force characteristics of the tire depend on the road surface condition largely. However, currently, it is difficult to detect tire performance degradation before the deterioration of vehicle dynamics in real time because tire force estimation is usually conducted by comparing the observed vehicle motion with the onboard reference vehicle-model motion. Such conventional estimators do not perform well if there is a significant difference between the vehicle and the model behavior. In this paper, a new tire state estimation method based on this tire longitudinal characteristic is proposed. In addition, the estimator for tire-road surface friction coefficient μ is proposed by using this geometric relationship. Using this method, the friction coefficient value for a real road can be determined from relatively simple calculations. Also, the advantage of this method is that it can be estimated in a small slip region before the tire loses its grip. In addition, this paper explain how to apply and the effect on the actual vehicle.

Cite this article as:
Y. Shiozawa, S. Tsukuda, and H. Mouri, “Proposal of the Tire Longitudinal Characteristics Real-Time Estimation Method,” J. Robot. Mechatron., Vol.30, No.5, pp. 811-818, 2018.
Data files:
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Last updated on Nov. 20, 2018