The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

• Published in: Energies (Basel) Vol. 17; no. 21; p. 5398
• Main Authors: Brusa, Alessandro, Shethia, Fenil Panalal, Petrone, Boris, Cavina, Nicolò, Moro, Davide, Galasso, Giovanni, Kitsopanidis, Ioannis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2024
• Subjects: Accuracy; Analysis; analytical functions; Calibration; Combustion; Data analysis; Datasets; effect superposition; Efficiency; Energy consumption; engine modeling; generalization; Machine learning; Neural networks; Optimization algorithms; Optimization techniques; Parameter estimation; Trends; Variables; United States; Pennsylvania
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17215398

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.