Braking torque estimation through machine learning algorithms

Braking torque estimation through machine learning algorithms

Federico BONINI, Alessandro RIVOLA, Alberto MARTINI

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Abstract. MotoGP class motorcycles rely on carbon braking system to cope with their incredible acceleration capability and high speed. Hence, assessing the torque generated by the front discs is a key to improve the vehicle performance. As direct measurement of the braking torque is not allowed during races, its value may be estimated through a physical model, using as inputs the brake fluid pressure (monitored on board), the braking system geometry and the friction coefficient (μ). However, the results obtained with this method are highly limited by the knowledge of the instantaneous friction coefficient between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model appears impractical to establish. This work aims to implement an innovative algorithm, based on machine learning, for determining μ from the signals regularly available in races, to enable accurate breaking torque computation. The proposed method consists of two main tools. An artificial neural network (ANN) is developed to approximate the unknown function that relates the input variables to μ, while a Kalman filter (KF) is implemented to estimate the real temperature distribution on the disc surface that constitutes one of the most important ANN inputs. The proposed algorithm has been successfully validated with real data collected from extensive tests in racetracks, with a special sensor setup.

Machine Learning, Carbon Brakes, Friction Coefficient

Published online 3/17/2022, 6 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Federico BONINI, Alessandro RIVOLA, Alberto MARTINI, Braking torque estimation through machine learning algorithms, Materials Research Proceedings, Vol. 26, pp 213-218, 2023


The article was published as article 35 of the book Theoretical and Applied Mechanics

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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