top of page


Mr. John Smith

Job title



The growth of electric propulsion systems motivates the automotive industry to transfer the focus from exhaust to non-exhaust emissions, with special attention to brake-related emissions. The literature lacks well-established approaches that describe the particulate emissions through reliable analytical correlations. Moreover, the mechanisms of brake particulate formation entail highly stochastic phenomena, which cannot be captured by means of traditional deterministic modelling tools. Machine learning algorithms have been recently used as an alternative method to seek for a branched correlation between tribological properties (i.e. friction coefficient and wear rate), pad composition, environmental and operating conditions. In this regard, the presented work focuses on the study and identification of sophisticated meta-models for the prediction of the number of emitted brake particulate. Specifically, artificial neural networks are developed and validated against brake emission data collected in real driving conditions at Technische Universität Ilmenau. The developed algorithms are intended for multiple use: (i) in the course of real driving emissions (RDE)testing, to support the experimental data; (ii) while driving, to inform the driver about the brake-related emission levels; (iii) as an on-board optimisation tool that identifies the brake actuation rules to minimise the release of particulate emissions.

Vincenzo Ricciardi, Research fellow, TU Ilmenau; Mr. Martin Schiele, TU Ilmenau; Mr. David Hesse, TU Ilmenau; Mr. Peter Hauschild, TU Ilmenau; Prof. Dr.-Ing. Klaus Augsburg, TU Ilmenau

Artificial Neural Network Regression Models for the Prediction of Brake-related Emissions

EB2020-STP-050 • Full Paper • EuroBrake 2020 • Fundamentals of Braking Technology


Sign up or login to the ICC to download this item and access the entire FISITA library.

Upgrade your ICC subscription to access all Library items.

Congratulations! Your ICC subscription gives you complete access to the FISITA Library.


Retrieving info...

Available for purchase on the FISITA Store


bottom of page