Evaluation of the Impact of Driving Cycle on the Fuel Consumption of Commercial Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The conditions of vehicle use are among the most important factors affecting the fuel consumption. Such conditions may include payload, type of duty cycle, traffic density, number of stops and starts, type of pavement, and use of auxiliary systems. Transport companies are interested in results from experiments reproducing similar operational conditions to help them understand and quantify the impact of duty cycles on fuel economy and operating costs. The goal of this study was to evaluate the effect of driving cycle on fuel efficiency. The fuel consumption measurement methodology was based on the protocols described in SAE J1321 Fuel Consumption Test Procedure - Type II and SAE J1526 Fuel Consumption Test Procedure (Engineering Method). The tests were conducted with various vehicles under different test conditions. Several duty cycles were replicated on the track, such as a local delivery, regional transport, long-distance constant speed, and stop-and-go cycles. The vehicles were driven by the same drivers throughout the respective test periods. The drivers followed the drive cycle, driving as they would normally do according to the road conditions and their driving skills. The drive cycle was monitored by observers assigned to each vehicle to assist the driver in following the cycle. The tests conducted on three different duty cycles in winter conditions showed a significant decrease (25 to 37%) in fuel efficiency between regional transport duty cycle and stop-and-go duty. A less significant decrease in fuel efficiency (1 to 7%) was noted between constant speed duty cycle at 80 km/h (50 mph) and regional transport duty cycle. An increase in fuel efficiency of 3.3% was reported for one of the heavy-duty vehicles on regional transport duty cycle compared to constant speed duty cycle. Summer tests resulted in an average decrease in fuel efficiency of 41.7% between constant speed duty cycle and stop-and-go aggressive urban highway duty cycle.</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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".