Artificial Intelligence Applications in Internal Combustion Engine Performance
Summary
Artificial intelligence has become instrumental in enhancing internal combustion engine performance through advanced data-driven modelling and optimisation techniques. Approaches such as artificial neural networks, support vector machines and deep learning enable rapid prediction of key performance metrics—including brake thermal efficiency, indicated mean effective pressure and emissions—under varying operating conditions and fuel compositions. These surrogate models reduce reliance on costly and time-consuming experimental and computational fluid dynamics tests by capturing complex nonlinear interactions between engine speed, load, fuel blends and injection strategies. Coupled with optimisation frameworks such as response surface methodology and evolutionary algorithms, AI techniques have driven significant gains in fuel economy and pollutant reduction, supporting stringent global emissions regulations and sustainable energy objectives. The growing integration of AI into engine control systems facilitates real-time calibration, adaptive control and predictive maintenance, underscoring its practical value across automotive, marine and power generation sectors worldwide.
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Artificial Intelligence Applications in Internal Combustion Engine Performance publication trend
The graph below shows the total number of articles in artificial intelligence applications in internal combustion engine performance across all publications each year (not limited to Nature Index journals).
Technical terms
Artificial neural network (ANN): A machine learning architecture inspired by biological neural networks, used to model complex nonlinear relationships between engine inputs and outputs.
Support vector regression (SVR): A supervised learning algorithm that constructs an optimal hyperplane for regression tasks, enabling accurate prediction of engine performance metrics.
Response surface methodology (RSM): A statistical and mathematical technique for optimising system responses by fitting polynomial models to experimental or simulated data.
Brake thermal efficiency (BTE): The ratio of an engine’s brake power output to the rate of fuel energy supplied, indicating the conversion efficiency of chemical to mechanical energy.
Indicated mean effective pressure (IMEP): The average pressure acting on the piston during the power stroke, used to quantify an engine’s indicated work output per cycle.
Brake specific fuel consumption (BSFC): The mass of fuel consumed per unit of brake power produced per hour, serving as a key indicator of engine fuel economy.
References
- AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition. Energy Conversion and Management X (2023).
- Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine. Alexandria Engineering Journal (2022).
- The Prediction of Spark-Ignition Engine Performance and Emissions Based on the SVR Algorithm. Processes (2022).
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