Are internally observable vehicle data good predictors of vehicle emissions?

• Published in: Transportation research. Part D, Transport and environment Vol. 77; pp. 252 - 270
• Main Authors: Fernandes, P., Macedo, E., Bahmankhah, B., Tomas, R.F., Bandeira, J.M., Coelho, M.C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2019
• Subjects: Diesel; Internally observable variables; On-road emissions; Portable emissions measurement system; Vehicle specific power; On-road emissions; Vehicle specific power; Diesel; Internally observable variables; Portable emissions measurement system
• ISSN: 1361-9209; 1879-2340
• DOI: 10.1016/j.trd.2019.11.004

•Second-by-second vehicle activity integrated with emission rates for DPVs.•Field measurements conducted in one Euro 4 and three Euro 6 using OBD and PEMS.•RPA and MPA allowed a good differentiation with respect to route trips.•IOV-based predictors of engine load showed to be good predictors of CO2.•NOX models were worse predictors since operating conditions of SCR were not considered. Scientific research has demonstrated that on-road exhaust emissions in diesel passenger vehicles (DPV) exceeds the official laboratory-test values. Increasing concern about the quantification of magnitude for these differences has meant an increasing use of Portable Emissions Monitoring System (PEMS), but the direct use of Internally Observable Variables (IOVs) can be useful to predict emissions. The motivation for this paper is to develop an empirical approach that integrates second-by-second vehicle activity and emission rates for DPV. The objectives of this research are two-fold: (1) to assess the effect of variation in acceleration-based parameters, vehicle specific power (VSP) and IOVs on carbon dioxide (CO2) and nitrogen oxides (NOx) emission rates; and (2) to examine the correlation between IOV-based predictors of engine load and VSP. Field measurements were collected from four DPV (two small, one medium and one multi-purpose) in urban, rural and highway routes using PEMS, Global Positioning System (GPS) receivers and On-board Diagnostic (OBD) scan tool, to measure real-world exhaust emissions and engine activity data. Results suggest the relative positive acceleration (RPA) and mean positive acceleration (MPA) allowed a good differentiation with respect to route trips. IOVs models based on the product of manifold absolute pressure (MAP) and engine revolutions per minute (RPM), and VSP showed to be good predictors of emission rates. Although the CO2 correlation was found to be good (R2 > 0.8), the models for NOx showed mixed results since some vehicles showed a reasonable correlation (R2 ~ 0.7) while others resulted in worst model predictions (R2 < 0.6). IOVs models have potential to be integrated into vehicle engine units and connected vehicles, for instance, to provide real-time information on emissions rates, but other parameters regarding the thermal management on after treatment system must be included in NOx prediction. This would allow for a better understanding of true physics behind NOx emissions in DPV.