This paper reviews the artificial intelligent algorithms in engine management. This study
provides a clear image of the current state of affairs for the past 15 years and provides fresh …

In the present study, a new combination of different available engine sensors along with an
exhaust gas temperature sensor is used as the input of an artificial neural network to …

Knock detection is critical to engine control as it prevents damage and ensures optimal
performance. However, it still presents significant challenges, particularly in alternative …

We introduce a novel approach to reconstructing the in-cylinder pressure trace from
vibration signals recorded with common knock sensors. The proposed methodology is …

This research aims to predict knock occurrences by deep learning using in-cylinder pressure
history from experiments and to elucidate the period in pressure history that is most …

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal
combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are …

This review explores machine learning approaches for predicting ignition delay in
combustion processes. Ignition delay is a vital parameter in optimizing the engine design …

Deep learning methods provide data-driven techniques for handling large amounts of
combustion data, thus finding the hidden patterns underlying these data. This study aims to …

In gasoline engines, the combustion process involves a flame's propagation from the spark
plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder …

The factors influencing the onset of knocking have a significant impact on how well a SI
engine performs. Hence, the efficacy in determining the onset and controlling of knock is a …