Heavy-duty diesel truck in-use NOx emissions evaluation using on-board sensors

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dc.contributor.author
Humphries, Kieran
Cooper, Coralie
Ahmadi, Mahdi
dc.date.accepted
2022-10-17
dc.date.accessioned
2024-05-27T19:40:10Z
dc.date.available
2024-05-27T19:40:10Z
dc.date.issued
2022-11-20
dc.date.submitted
2022-04-29
dc.description.abstract - en
Governments and regulatory agencies in North America are evaluating the nitrogen oxides (NO<sub>x</sub>) emissions of heavy-duty on-road vehicles to effectively regulate these emissions in order to improve public health and meet air quality requirements. This paper provides results from real-world Class 8 tractor-trailer truck activity and emissions data gathering conducted in the Northeast and Mid-Atlantic United States. Unlike some other areas of the United States (US), there is little available data on in-use operation and emissions performance from heavy-duty trucks in this region where temperatures can be consistently cold in winter. The purposes of this study are to add to the literature on real-world truck operation and emissions in the Northeast and Mid-Atlantic regions; to analyze the captured emissions data using recently established calculation methods implemented by the California Air Resources Board (CARB), which have not yet been applied to data from this region; and to assist air quality regulators in identifying priorities for new heavy-duty engine and vehicle emission standards and test procedures. The Northeast States for Coordinated Air Use Management (NESCAUM) conducted this project jointly with Environment and Climate Change Canada (ECCC). Given the proximity of the truck routes evaluated in this project to the Eastern Canadian provinces, the information is of interest to Canadian regulators as well. The results of this data logging and analysis showed that the CARB’s three-bin moving average window (MAW) and sum-over-sum NO<sub>x</sub> emissions calculations can be successfully applied to in-use truck data sets. However, the use of the on-board vehicle NO<sub>x</sub> sensors for data logging limited the amount of time that data were able to be captured at low loads. The active percentage time of sensors by truck was between 43% and 80% in low-load conditions (69% over all trucks). Therefore, low-load data was “backfilled” with estimated emissions values in order to compensate for the time the sensors were inactive. Backfilled data showed that a range of 21% to 67% of the mass of NO<sub>x</sub> emitted by individual trucks occurred in idle and low-load conditions combined, whereas the original data showed values between 10% and 43%. Median daily backfilled sum-over-sum NO<sub>x</sub> emissions results by truck specimen ranged from 0.32 g/bhp-hr to 0.75 g/bhp-hr in low-load conditions and from 0.02 g/bhp-hr to 0.16 g/bhp-hr in high-load conditions. As part of the study, data on over 150 vehicle and engine parameters during 100 days of truck operation were collected. This data can be used by regulators and researchers to evaluate truck emissions and operations in the region.
dc.identifier.doi
https://doi.org/10.4271/2022-01-5098
dc.identifier.issn
0148-7191
2688-3627
dc.identifier.uri
https://open-science.canada.ca/handle/123456789/2523
dc.language.iso
en
dc.publisher
SAE International
dc.rights - en
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights - fr
Creative Commons Attribution - Pas d'utilisation commerciale - Pas de modification 4.0 International (CC BY-NC-ND 4.0)
dc.rights.openaccesslevel - en
Gold
dc.rights.openaccesslevel - fr
Or
dc.rights.uri - en
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.uri - fr
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.fr
dc.subject - en
Nature and environment
Science and technology
dc.subject - fr
Nature et environnement
Sciences et technologie
dc.subject.en - en
Nature and environment
Science and technology
dc.subject.fr - fr
Nature et environnement
Sciences et technologie
dc.title - en
Heavy-duty diesel truck in-use NOx emissions evaluation using on-board sensors
dc.type - en
Article
dc.type - fr
Article
local.article.journaltitle
SAE Technical Paper
local.pagination
12 pages
local.peerreview - en
Yes
local.peerreview - fr
Oui
local.requestdoi
No
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