Modeling the emissions of a gasoline engine during high-transient operation using machine learning approaches

To perform a suitable optimization method in terms of emission and efficiency for an internal combustion engine, first highly accurate and possible real-time capable modeling for the transient operations should be provided. In this work, the modeling of NO _x and HC raw emission (before exhaust aftertreatment systems) in a six-cylinder gasoline engine under highly transient operation was performed using machine learning approaches. Three different machine learning methods, namely Artificial Neural Network, Long Short-Term Memory, and Random Forest were used and the results of these models were compared with each other. In general, the results show a significant improvement in accuracy compared to other studies that have modeled transient operations. Furthermore, the shortcoming of Artificial Neural Network for the prediction of the HC emission by the transient operation is observed. The coefficient of determination (R²) for the best model for NO _x prediction is 0.98 and 0.97 for the training data and test data, respectively. This value is 0.9 and 0.89 for the best HC prediction model.