Simulation Methodology for Duty Cycle based Fuel Consumption Calculation for Heavy Commercial Vehicles
Bibliographic record
Abstract
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 CO2 intensity by 33% - 35% by 2030 below the 2005 levels [1], 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. Transportation sector is majorly responsible for the GHG Emission of which the CO2 emission from commercial vehicles is nearly 73% [2], 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/CO2 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 CO2 monitoring and certification [3]. This paper illustrates the methodology for CO2 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 & Tyres) testing were carried out as per defined methodology to generate the detailed vehicle data to prepare the simulation model in VECTO [4]. The results from VECTO are then compared with the real world fuel consumption measurements. 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
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".