Real Road Transient Driving Cycle Simulations in Engine TestBed for Fuel Economy Prediction
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The present work describes an approach to predict the vehicle fuel economy by simulating its engine drive cycle on a transient engine dynamometer in an engine testbed. The driving cycles investigated in the current study were generated from the typical experimental data measured on different vehicles ranging from Intermediate Commercial Vehicle (ICV) to Heavy-duty Commercial Vehicle (HCV) in real-world traffic conditions include various cities, highways and village roads in India.</div><div class="htmlview paragraph">Reliability and robustness of the method was studied on various engines with cubic capacity from 3.8 liters to 8 liters using different drive cycles, and the results were discussed. Later, using same measured drive cycles, vehicle fuel economy was predicted by a vehicle simulation tool (AVL CRUISE) and results were compared with experimental data. In addition, engine coolant temperature effect on fuel economy was investigated.</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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".