A Deep Learning Method for the Prediction of Pollutant Emissions from Internal Combustion Engines

Abstract

The increasing demand for vehicles is leading to a rise in pollutant emissions across the world. This decline in air quality is significantly impacting public health, with internal combustion engines being a major contributor to this concerning trend. Ever-stringent regulations demand high engine efficiency and reduced pollutant emissions. Therefore, every automobile company requires rigorous methods for accurately estimating engine emissions. The implementation of advanced technologies, including machine learning methods, has proven to be a promising solution. The present work aims to develop an artificial intelligence-based model to estimate the pollutant emissions produced by an internal combustion engine under varying operating conditions. Experimental activities have been conducted on a single-cylinder spark ignition research engine with gasoline port fuel injection under both stationary and dynamic operating conditions. This work explores different artificial intelligence architectures and compares their performance in order to determine the best approach for the presented task. These structures have been trained and tested based on data obtained from the engine control unit and fast emission analyzer. The main target is to evaluate the possibility of applying the presented artificial intelligence predictive model as an on-board virtual tool in the estimation of emissions in real driving conditions.

Keywords

• pollutant emissions
• air quality
• internal combustion engine
• port fuel injection–spark ignition engine
• artificial intelligence
• machine learning