The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm

Abstract

Engine development needs to reduce costs and time. As the current main development methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time-consuming task. Therefore, this study aims to verify the applicability of the machine learning (ML) method in the prediction of engine efficiency and emission performance. The support vector regression (SVR) algorithm was chosen for this paper. By the selection of kernel functions and hyperparameters sets, the relationship between the operation parameters of a spark-ignition (SI) engine and its economic and emissions characteristics was established. The trained SVR algorithm can predict fuel consumption rate, unburned hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. The determination coefficient (R2) of experimental measured data and model predictions was close to 1, and the root-mean-squared error (RMSE) is close to zero. Additionally, the SVR model captured the corresponding trend of the engine with the input, though some existed small errors. In conclusion, these results indicated that the SVR model was suitable for the applications studied in this research.