The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines

Abstract

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R2) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

Integrating Machine Learning for Predicting Internal Combustion Engine Performance and Segment-Based CO2 Emissions Across Urban and Rural Settings

“…The primary objective was to forecast the optimal operational settings to enhance performance and minimize emissions. Yang et al [26] compared three machine learning models to predictive performance in forecasting indicated mean effective pressure indicator with the input parameters spark timing, speed, and load. For the prediction of engine related parameters, the prediction accuracy and effect of artificial neural networks, random and support vector machine was good.…”

Artificial Neural Network-Based Modeling of Performance Spark Ignition Engine Fuelled with Bioethanol and Gasoline

“…Furthermore, machine learning can assess alternative engine designs and control strategies in order to enhance performance and minimize emissions. Research findings indicate that machine learning models can generate highly precise predictions and prove invaluable in the optimization of engine performance and reduction of exhaust emissions [14]. Certain researchers utilize machine learning for forecasting engine performance in smart vehicles [15].…”