Evaluation of Engine Programming to Reduce Fuel Consumption
Bibliographic record
Abstract
The goal of this study was to evaluate the potential for reducing fuel consumption of heavy-duty vehicles by modifying their engine power ratings through programming of their engine electronic control units. This paper summarizes the activities, observations, and results obtained from tests conducted on a test track to compare default settings with those optimized for potential economic gains and improved performance. Tests for long-distance and regional transport operations were conducted at constant speed. Results from these tests showed considerable fuel savings, of approximately 7%, when the engine power rating was reduced from 450 hp to 400 hp and speed from 105 km/h to 98 km/h. In these tests, the dynamic performance of the vehicles was not affected by the reduction in power. Tests for local delivery operations were conducted on a stop-and-go cycle and showed fuel savings of 4.5% when the engine power rating was reduced from 450 hp to 400 hp, with the driver using a moderate driving technique. Tests for urban transport operations were conducted on stop-and-go cycles and showed fuel savings of up to 5% when the engine power rating was reduced from 240 hp to 200 hp, with the driver using an eco-friendly driving technique. It is certain that limiting the available engine power will impact vehicle performance, depending on driving style. Consequently, technical considerations must be accompanied by training drivers, especially those with aggressive driving habits, to practice eco-friendly driving techniques. This training could be incorporated into a process of continuous improvement and monitoring of driver performance as part of incentive programs.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".