Simulation Methodology for Duty Cycle based Fuel Consumption Calculation for Heavy Commercial Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Automobile industry is facing challenges in the field of technological innovation and achieving minimum Total Cost of Ownership (TCO) despite rise in fuel prices. To overcome these challenges is certainly a challenging task. In doing so, automobile sector is mainly focused on passenger safety, comfort, reliability, meeting stringent emission norms, and above all reducing the vehicle fuel consumption. Referring to the Paris climate agreement, and India’s commitment to reduce the CO<sub>2</sub> intensity by 33% - 35% by 2030 below the 2005 levels [<span class="xref">1</span>], it is imperative to lay down strong policies and procedure to curb the fuel consumption to contribute for reduction in carbon foot print and oil imports.</div><div class="htmlview paragraph">Transportation sector is majorly responsible for the GHG Emission of which the CO<sub>2</sub> emission from commercial vehicles is nearly 73% [<span class="xref">2</span>], although the total sales of commercial vehicles are around 4% of cumulative vehicle sales. Physical testing of these vehicles for FC measurement is very expensive and laborious task due to number of variants involved in testing. In view of these factors, it is necessary to establish a very robust simulation based methodology for fuel consumption/CO<sub>2</sub> monitoring of commercial vehicles with GVW of above 3.5 T. Countries like US, EU, Canada, Japan and China have already moved towards simulation based calculations for CO<sub>2</sub> monitoring and certification [<span class="xref">3</span>].</div><div class="htmlview paragraph">This paper illustrates the methodology for CO<sub>2</sub> calculation, which is in harmony with the FC prediction and monitoring procedure available in Europe. Strenuous vehicle (air drag test, coast down) testing and component (Engine, Transmission, Differential &amp; Tyres) testing were carried out as per defined methodology to generate the detailed vehicle data to prepare the simulation model in VECTO [<span class="xref">4</span>]. The results from VECTO are then compared with the real world fuel consumption measurements.</div><div class="htmlview paragraph">To make the simulation tool more compatible with Indian driving and road conditions, it is also proposed to develop India specific mission profiles which would bring in a more holistic approach for fuel consumption estimation by simulation</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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".