AI for brake related, non-exhaust emissions

AI for brake related, non-exhaust emissions

Artificial Neural Network regression models for the prediction of brake-related emissions

Abstract

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 stochastic meta-models for the prediction of the number of emitted brake particulate and associated uncertainty. 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.

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