Evaluation of the Impact of Driving Cycle on the Fuel Consumption of Commercial Vehicles
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| 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".