Development of Mission Profile Based Simulation Methodology for Fuel Consumption Prediction and Validation for Light, Medium and Heavy Commercial Vehicles
Bibliographic record
Abstract
"The very main objective for any innovative process to begin is the necessity. In doing so, the need of the automobile sector is mainly focused on passenger safety, comfort, reliability and above all the most defining factor would be to reduce fuel consumption (FC). In view of the Paris climate agreement in December 2015 and India’s commitment towards the ratification of the agreement to reduce the emission intensity of the GDP by 33%-35% by 2030 below 2005 levels and to create a cumulative carbon sink of 2.5 to 3.0 billion CO2 equivalent by 2030 about 36% compared to the 1990's levels, it is imperative to lay down strong policies and procedure to curb the fuel consumption and thereby reducing the carbon foot print. The growing imports on crude oil and the increasing CO2 emission per capita activity are major concern for authorities. In India one of the key sector which is responsible for the Greenhouse Gas Emission(GHG) contribution is transportation sector, of which road transportation alone contributes nearly 73% of overall GHG’s emission. Further bifurcation, it was found that the highest contribution of CO2 emission is from commercial vehicles, although the sales figures for this segment just hover around 4% of the overall annual sales volume of all vehicles. In view of the above factors, the regulation to bring a very robust methodology for the CO2 monitoring of commercial vehicle above Gross Vehicle Weight of 3.5-ton and possibility of making it a mandatory procedure is in progress. The fuel consumption measurement approach by simulation methodology shall be a substitute for the present legislation on constant speed fuel consumption (CSFC) where, the FC test would be carried only for defined speeds of 40,50 and 60 km/hr. As most of the countries like US, EU, Japan, China and Canada have moved towards mission profile based simulation for FC certification. India has also initiated the measures for simulation based FC prediction. As a precursor for simulation based FC prediction, in this paper we have followed a methodology which is comparable to the present FC prediction and monitoring procedure available in Europe. This pilot project involves strenuous testing of all the individual components of a vehicle as per defined methodology. With these inputs, we carried out simulation and compared with the real world fuel consumption. The results of the study revealed variations in the simulation compared to on-road test results. These deviations were due to the default table values in the software that are in-built which are more suited for the European conditions than for Indian conditions. To make the simulation tool more compatible with Indian driving and road conditions, it is proposed to have detailed study on vehicle acceleration limit, deceleration limit, gear shift pattern, driver behavior, auxiliary power consumption and above all, to formulate India specific mission profile which would be more relevant for bringing a more holistic fuel consumption prediction approach by simulation methodology."
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".