Development of Mission Profile Based Simulation Methodology for Fuel Consumption Prediction and Validation for Light, Medium and Heavy Commercial Vehicles
Notice bibliographique
Résumé
"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."
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».