Influence of vehicle mass on RDE tailpipe emissions from over 1000 vehicles

A wide variety of factors can affect emissions from a vehicle. In this study, carbon dioxide and pollutant emissions (carbon monoxide, particle number, and nitrogen oxides (NOx)) from over 1000 vehicles are measured over Real Driving Emissions (RDE) test cycles from vehicles spanning the Euro 3 to Euro 6d regulations. These are presented in order to observe fleet-wide correlations. Vehicle mass is found to be a strong predictor of carbon dioxide emissions but a weak predictor of pollutant emissions. In addition, non-regulated pollutants including nitrous oxide (N2O), formaldehyde, combined aromatics, alkanes, PAHs and alcohols are measured and presented. Overall, there is no obvious link between higher vehicle mass and these unregulated emissions. In this paper, the link between other vehicle characteristics - Euro emissions classification, powertrain type (gasoline/diesel/hybrid etc), and engine size will also be explored. The findings show that vehicle mass is a good predictor for carbon dioxide emissions, but not for pollutant and other non-regulated emissions. Engine size is also a good predictor of carbon dioxide emissions. However, there are no other vehicle characteristics that can predict the pollutant emissions particularly better either. Vehicle mass may also be a useful indicator of other measures of the environmental impact of a vehicle. This may lead towards a future vehicle regulation system where in a world of diverse powertrains, mass can be used.

Publication Details

Journal
Oxford University Research Archive (ORA) (University of Oxford)
Published
2025-01-01
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Influence of vehicle mass on RDE tailpipe emissions from over 1000 vehicles

Oxford University Research Archive (ORA) (University of Oxford)
Vehicle emissions and performance
article

Influence of vehicle mass on RDE tailpipe emissions from over 1000 vehicles

article en

Abstract

A wide variety of factors can affect emissions from a vehicle. In this study, carbon dioxide and pollutant emissions (carbon monoxide, particle number, and nitrogen oxides (NOx)) from over 1000 vehicles are measured over Real Driving Emissions (RDE) test cycles from vehicles spanning the Euro 3 to Euro 6d regulations. These are presented in order to observe fleet-wide correlations. Vehicle mass is found to be a strong predictor of carbon dioxide emissions but a weak predictor of pollutant emissions. In addition, non-regulated pollutants including nitrous oxide (N2O), formaldehyde, combined aromatics, alkanes, PAHs and alcohols are measured and presented. Overall, there is no obvious link between higher vehicle mass and these unregulated emissions. In this paper, the link between other vehicle characteristics - Euro emissions classification, powertrain type (gasoline/diesel/hybrid etc), and engine size will also be explored. The findings show that vehicle mass is a good predictor for carbon dioxide emissions, but not for pollutant and other non-regulated emissions. Engine size is also a good predictor of carbon dioxide emissions. However, there are no other vehicle characteristics that can predict the pollutant emissions particularly better either. Vehicle mass may also be a useful indicator of other measures of the environmental impact of a vehicle. This may lead towards a future vehicle regulation system where in a world of diverse powertrains, mass can be used.

Oxford University Research Archive (ORA) (University of Oxford)
Openalex Percentile: Top 51%
Vehicle emissions and performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.