Artificial Neural Networks for In-Cycle Prediction of Knock Events

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6 Scopus citations

Abstract

Downsized turbocharged engines have been increasingly popular in modern light-duty vehicles due to their fuel efficiency benefits. However, high power density in such engines is achieved thanks to high in-cylinder pressure and temperature conditions that increase knock propensity. Next-cycle control has been studied as a method to reduce the damaging effects of knock by operating the engine in a low knock probability condition. This exploratory study looks at the feasibility of in-cycle knock prediction as a tool for advanced knock control algorithms. A methodology is proposed to 1) choose in-cycle features of the pressure trace that highly correlate with knock events and 2) train artificial neural networks to predict in-cycle knock events before knock onset. The methodology was validated at different operating conditions and different levels of generalization. Precision and recall were used as metrics to evaluate the binary classifier. However, the Fowlkes-Mallows (FM) index was used to compare the result of the clustering algorithm at different operating conditions. The results showed a maximum FM index of 0.7 when the prediction was done at knock onset and a minimum FM index of 0.45 when the prediction was done at spark timing.

Original languageEnglish
JournalSAE Technical Papers
Issue number2022
DOIs
StatePublished - Mar 29 2022
EventSAE 2022 Annual World Congress Experience, WCX 2022 - Virtual, Online, United States
Duration: Apr 5 2022Apr 7 2022

Funding

This research was conducted as part of the Partnership to Advance Combustion Engines (PACE) Consortium sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO). The PACE Consortium is a collaborative project of multiple National Laboratories that combines unique experiments with world-class DOE computing and machine learning expertise to speed discovery of knowledge, improve engine design tools, and enable market-competitive powertrain solutions with potential for best-in-class lifecycle emissions. A special thanks to DOE VTO program managers Mike Weismiller and Gurpreet Singh.

FundersFunder number
U.S. Department of Energy

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