Machine learning-based estimation of gaseous and particulate emissions using internally observable vehicle operating parameters

Measuring vehicular emissions is crucial for emission management and air quality control. However, conventional measurement equipment is costly and requires continuous maintenance. This study aims to address these challenges by introducing an Artificial Neural Network (ANN)-based methodology that us...

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Bibliographic Details
Published in:Urban climate 2023-11, Vol.52, p.101734, Article 101734
Main Authors: Seo, Jigu, Lim, Yunsung, Han, Jungwon, Park, Sungwook
Format: Article
Language:English
Subjects:
Artificial neural network
Emission map
Gaseous emissions
Particulate emissions
Vehicle emission model
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Summary:Measuring vehicular emissions is crucial for emission management and air quality control. However, conventional measurement equipment is costly and requires continuous maintenance. This study aims to address these challenges by introducing an Artificial Neural Network (ANN)-based methodology that using internally observable vehicle operating parameters to estimate emissions of particulate number (PN), nitrogen oxide (NOx), carbon monoxide (CO), and carbon dioxide (CO2). The research was conducted in three steps: (1) analyzing the correlations between vehicle operating parameters and exhaust emissions through emission map analysis, (2) developing and validating artificial neural networks (ANNs), and (3) examining the impact of vehicle driving conditions on emission predictions. The ANNs were rigorously validated across various operating and ambient conditions, including different temperatures, cold and warmed-up starts, and various driving scenarios. The prediction accuracy for CO2 is higher than other exhaust gases, with an error of
ISSN:2212-0955
2212-0955
DOI:10.1016/j.uclim.2023.101734