Applications of Machine Learning Algorithms to Internal Combustion Engine Studies

Abstract

Petroleum (diesel, gasoline) reserves are depleting as energy demand rises, and the quest for conventional fuels is growing daily. Internal combustion engine research is crucial because of this. Current research on internal combustion engines is expensive, both in terms of setting up the experiment and in terms of the fuels that are employed and consumed. Because of this, machine learning techniques have been used in recent years to estimate engine performance and exhaust emissions. As a result, less time and material are utilized, and high accuracy in estimating the engine's performance and fuel-related exhaust emissions is attained. Machine learning algorithms will be discussed in this study first, followed by an assessment of recent research and findings in the literature.

Keywords

• Machine Learning
• IC Engines
• Alternative Fuels
• Performance
• Exhaust Emissions

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