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**Harvard**

Prokes, J., Albinsson, A. och Laine, L. (2016) *Quantification of Excitation Required for Accurate Friction Estimation*.

** BibTeX **

@conference{

Prokes2016,

author={Prokes, Jakub and Albinsson, Anton and Laine, Leo},

title={Quantification of Excitation Required for Accurate Friction Estimation},

booktitle={19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016;Rio de Janeiro; Brazil; 1-4 November 2016},

isbn={9781509018895},

pages={Art no 7795966, Pages 2551-2558},

abstract={Real-time information about the current tyre-road friction coefficient is important for vehicles with increasing levels of automation to adapt intervention thresholds and vehicle velocity. Online friction estimation poses large challenges in terms of availability and accuracy of the estimate. When estimating the friction based on the slip and force of a
tyre, the accuracy of the estimate depends on the current tyre friction utilisation. This paper presents a method to evaluate how large tyre force excitation that is required to estimate the current friction level within a given error using an effectbased approach. The results indicate that large variations in the required utilisation should be expected for different tyre and road surface combinations when tyre models that only
have the slip stiffness and the friction coefficient as parameters (e.g. brush tyre model) are used in the observer to estimate the friction coefficient. For the brush tyre model it varied from 54% in the best case to 94% in the worst case.
To illustrate the benefit of quantification of required utilisation an example use of an estimator based on the brush tyre model is shown in the end of this paper. A simulation is performed to show how fast a sudden drop in friction during braking can be detected using a recursive online estimator. The
results show that the estimator is able to detect the change in friction and converge to the new friction level in 0.06 s, which is fast enough to potentially improve the performance of ABS systems of trucks.
Tyre models for online friction estimation must hence be chosen with the intended use in mind. The results show that the friction information estimated using the brush tyre model at larger excitation levels is reliable and can therefore be shared with other road users in order to improve the overall safety and efficiency of the transportation system.},

year={2016},

keywords={Vehicle Dynamics, Tyre-road friction estimation, State estimation},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 241863

A1 Prokes, Jakub

A1 Albinsson, Anton

A1 Laine, Leo

T1 Quantification of Excitation Required for Accurate Friction Estimation

YR 2016

T2 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016;Rio de Janeiro; Brazil; 1-4 November 2016

SN 9781509018895

SP 77959662551

AB Real-time information about the current tyre-road friction coefficient is important for vehicles with increasing levels of automation to adapt intervention thresholds and vehicle velocity. Online friction estimation poses large challenges in terms of availability and accuracy of the estimate. When estimating the friction based on the slip and force of a
tyre, the accuracy of the estimate depends on the current tyre friction utilisation. This paper presents a method to evaluate how large tyre force excitation that is required to estimate the current friction level within a given error using an effectbased approach. The results indicate that large variations in the required utilisation should be expected for different tyre and road surface combinations when tyre models that only
have the slip stiffness and the friction coefficient as parameters (e.g. brush tyre model) are used in the observer to estimate the friction coefficient. For the brush tyre model it varied from 54% in the best case to 94% in the worst case.
To illustrate the benefit of quantification of required utilisation an example use of an estimator based on the brush tyre model is shown in the end of this paper. A simulation is performed to show how fast a sudden drop in friction during braking can be detected using a recursive online estimator. The
results show that the estimator is able to detect the change in friction and converge to the new friction level in 0.06 s, which is fast enough to potentially improve the performance of ABS systems of trucks.
Tyre models for online friction estimation must hence be chosen with the intended use in mind. The results show that the friction information estimated using the brush tyre model at larger excitation levels is reliable and can therefore be shared with other road users in order to improve the overall safety and efficiency of the transportation system.

LA eng

DO 10.1109/ITSC.2016.7795966

LK http://dx.doi.org/10.1109/ITSC.2016.7795966

OL 30