Comparison of Pollutant Emissions from Common Platform Vehicles Operating on Alternative Fuels over a Range of Driving Cycles at Standard and Cold Ambient Temperatures
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Alternative fuels and power trains are expected to play an important role in reducing emissions of greenhouse gases (GHGs) and other pollutants. In this study, five light-duty vans, operating on alternative fuels and propulsion systems, were tested on a chassis dynamometer for emissions and efficiency. The vehicles were powered with Tier 2 gasoline, low blend ethanol (E10), compressed natural gas (CNG), liquefied petroleum gas (LPG), and an electric battery. Four test cycles were used representing city driving and cold-start (FTP-75), aggressive high speed driving (US06), free flow highway driving (HWFCT), and a combination of urban, rural, and motorway driving (WHVC). Tests were performed at a temperature of 22°C, with select tests at -7°C and -18°C.</div><div class="htmlview paragraph">Exhaust emissions were measured and characterized including CO, NO<sub>X</sub>, THC, PM and CO<sub>2</sub>. On the FTP-75, WHVC, and US06 cycles additional exhaust emission characterization included N<sub>2</sub>O, and CH<sub>4</sub>. On the FTP-75, carbonyl compounds and volatile organic compounds (VOCs) were also characterized. Fuel and energy consumption, CO<sub>2,e</sub> and NMOG emissions were calculated.</div><div class="htmlview paragraph">The emissions impact of alternative fuels varied with temperature and driving cycle. Compared to conventional gasoline, the use of alternative fuels generally resulted in reduced CO<sub>2</sub> equivalent emission rates: 12-14% reduction with the use of LPG fuel, 18-21% reduction with the use of CNG fuel, and 60-75% reduction with the use of battery electric propulsion (assuming the average Canadian mix for electricity generation). With E10 fuel, the reductions in tailpipe CO<sub>2</sub> equivalent emission rate were generally not statistically significant. Results for other regulated and unregulated emissions varied, and depended on driving cycle and temperature.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".