Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

Maksymilian Mądziel ()
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Maksymilian Mądziel: Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland

Energies, 2024, vol. 17, issue 19, 1-18

Abstract: In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO 2 , CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R 2 : 0.76), while polynomial regression excelled for CO 2 (R 2 : 0.93). For warm engines, polynomial regression performed best for CO 2 (R 2 : 0.95), and gradient boosting delivered results for THC (R 2 : 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

Keywords: vehicles; emission; modeling; artificial intelligence; portable emission measurement system; combustion engines (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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