Evaluation of the Effect of Ambient Conditions on the Fuel Consumption of Commercial Vehicles
Bibliographic record
Abstract
Commercial fleets are interested in results from experiments conducted in real operational conditions to help them quantify and understand the impact of environmental factors on fuel economy and operating costs. The goal of this study was to measure through controlled track testing and operational testing the effects of environmental conditions, particularly ambient temperature, and air density, on fuel consumption. Extensive track testing based on the SAE J1321 Fuel Consumption Test Procedure - Type II protocol with various vehicles under different test conditions showed a decrease in fuel efficiency of up to 12% for an air density variation of 7% and an ambient temperature variation of 30 °F (17 °C). Data from various and extensive operational tests were also analyzed, specifically from tests conducted using several groups of medium and heavy-duty vehicles involved in regional, local, urban transport and pick-up and delivery. The tests were conducted over several months of the warm season, followed by several months of the cold season. Operational tests conducted over nine months with twelve medium and heavy-duty vehicles indicated a decrease in fuel efficiency of 9 to 31% for a temperature decrease of 43 0F (24 0C), depending on operation and type of vehicle. Other operational tests conducted over one year in two different geographic xmlns:xlink="http://www.w3.org/1999/xlink" and climatic regions resulted in 18% difference in average fuel consumption for a temperature decrease of 36 0F (20 °C), and 6% difference in average fuel consumption for a temperature decrease of 16 0F (9 °C), respectively. Fuel consumption increases with increasing air density, and fuel efficiency increases with increasing ambient temperature. As the temperature gets colder during winter, additional factors come into play that further reduce fuel economy, such as wind, rain, and snow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".