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.
Artificial Neural Networks for In-Cycle Prediction of Knock Events
Sae Technical Papers
WCX SAE World Congress Experience ; 2022
2022-03-29
Aufsatz (Konferenz)
Englisch
Artificial Neural Networks for In-Cycle Prediction of Knock Events
British Library Conference Proceedings | 2022
|FULL-LENGTH PAPERS - Limit Cycle Oscillation Prediction Using Artificial Neural Networks
Online Contents | 2001
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