Deep Learning-based Modeling of Gasoline Controlled Auto-Ignition

Abstract

Internal combustion engines face increasingly stringent pollution and greenhouse gas emission restrictions. In this regard, the technology of low-temperature combustion is the scope of research, as it can reduce pollutant emissions while increasing engine efficiency. However, the system dynamics are very complicated and have a high sensitivity concerning the thermodynamics in the combustion chamber. Modeling with first principle models would be time-consuming and requires accurate parameter estimations. Therefore, this work presents deep learning-based models to capture the dynamics of the cylinder pressure trace for gasoline controlled auto-ignition. These models trained with experimental data can learn the internal pattern of the combustion cycle dynamics. Based on information from the current cycle, they enable the prediction of the combustion pressure trace for the following. The results show that the models precisely predict the pressure traces and ensure high prediction accuracy of engine performance parameters derived from the predicted traces. R2 values of 0.95 or more are obtained for global parameters, whereas values of 0.8 or more are obtained for local parameters.