Scholarly record
STAGES OF ENGINE RUN-UP TIME MODELING USING AI
Abstract
This paper presents a theoretical analysis of the application of artificial neural networks for modeling the run-up time of combustion engines. Engine run-up time is defined as the period required for the crankshaft rotational speed to increase from an initial state to a specified operating level. The process is influenced by numerous nonlinear physical and chemical phenomena, including fuel properties, combustion dynamics, mechanical inertia, and operating conditions. Due to the complexity of these interactions, the development of accurate deterministic mathematical models is difficult and often requires significant simplifications. In the proposed approach, the engine run-up process is treated as a black-box system in which selected fuel parameters and operating conditions are mapped directly to engine run-up time. The analyzed input variables include fuel calorific value, viscosity, fuel droplet burning time, vapor pressure, and selected operating parameters. Artificial neural networks are applied as a modeling tool because of their ability to approximate highly nonlinear relationships without requiring a complete physical description of the process. The paper discusses the structure and operation of multilayer neural networks, including forward propagation, error calculation, backpropagation, and weight updating procedures. Advantages and limitations of the proposed approach are also analyzed. The study demonstrates that neural-network-based models offer high flexibility and strong approximation capabilities, although their performance strongly depends on the quality of experimental data and proper overfitting prevention methods. The presented analysis confirms the significant potential of artificial intelligence methods in combustion engine diagnostics, optimization, and predictive modeling.
Publication details
References7
Adamiak B., Andrych-Zalewska M., Ch?opek Z., Lasocki J., Merkisz J., Quantifying fuel consumption in internal combustion engine through pollutant emission analysis: Interlaboratory comparison, Advances in Science and Technology Research Journal, Poland, 2026, pp. 505-517, ISSN 2299-8624, DOI: 10.12913/22998624/215000;
Sitnik L.J., Andrych-Zalewska M., Method of Comparative Analysis of Energy Consumption in Passenger Car Fleets with Internal Combustion, Hybrid, Battery Electric, and Hydrogen Powertrains in Long-Term European Operating Conditions, Energies, Switzerland, 2026, Article 616, ISSN 1996-1073, DOI: 10.3390/en19030616;
?mudka Z., Postrzednik S., Zastosowanie rachunku wyrównawczego do wyznaczania parametrów obiegu porównawczego silnika spalinowego, Politechnika ?l?ska, Gliwice, Poland, 2008;
Reitz R.D., Foster D., Ghandhi J., Rothamer D., Rutland C., Trujillo M., Sanders S., Optimization of Advanced Diesel Engine Combustion Strategies, Office of FreedomCAR and Vehicle Technologies Program Merit Review Meeting, Washington, DC, USA, 2010;
Kardasz P., Doskocz J., Janiczek T., Jaworska E., Wysocka J., Alternatywne paliwa dla flot, Polski Instytut Eko Energii Sp. z o.o., Wroc?aw, Poland, 2016;
Sitnik L.J., Ekopaliwa silnikowe, Oficyna Wydawnicza Politechniki Wroc?awskiej, Wroc?aw, Poland, 2004, ISBN 8370857671;
Holman J.P., Experimental Methods for Engineers, McGraw-Hill Education, USA, 2017, ISBN 978-1259873614.