Feasibility Study of Medium- and Heavy-Duty Compressed Renewable/Natural Gas Vehicles in Canada
Notice bibliographique
Résumé
Abstract Concerns about environmental degradation and finite natural resources necessitate cleaner sources of energy for use in the transportation sector. In Canada, natural gas (NG) is currently being appraised as a potential alternative fuel for use in vehicles for both medium- and heavy-duty use due to its relatively lower costs compared with that of conventional fuels. The idea of compressed natural gas vehicles (CNGVs) is being mooted as inexpensive for fleet owners and especially because it will potentially significantly reduce harmful emissions into the environment. A short feasibility study was conducted to ascertain the potential for reduced emissions and savings opportunities presented by CNGVs and renewable NGVs (RNGVs) in both medium- and heavy-duty vehicles. The study which is discussed in the present paper was carried out on long-haul trucking and refuse trucks, respectively. Emphasis was laid on individual vehicle operating economics and emissions reduction, and the identification of practical considerations for both the individual application and CNGVs/RNGVs as a whole. A financial analysis of the annual cost savings that is achievable when an individual diesel vehicle is replaced with a CNG vehicle was also presented. This paper drew substantial references from published case studies for relevant data on maintenance costs, fuel economy, range, and annual distance traveled. It relied on a summary report from Argonne National Laboratory’s GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) (Argonne National Laboratory, 2018, Argonne GREET Model, US Department of Energy. https://greet.es.anl.gov/. Accessed October 5, 2020) for its discussion on relative fuel efficiency penalties for heavy-duty CNGVs. The fuel cost figures were mostly drawn from motor fuel data of the Ontario Ministry of Transportation since the Ministry is one of the few available sources of compressed natural gas fuel prices. Finally, the GHGenius life-cycle analysis tool ((S&T) Squared Consultants Inc., 2019, GHGenius, (S&T) Squared Consultants Inc. https://www.ghgenius.ca. Accessed October 5, 2020) was employed to determine fuel-cycle emissions in Canada for comparison purposes. The study produced remarkable findings. Results showed that compared with diesel-fuelled vehicles, emissions in CNG heavy-and-medium-duty vehicles reduced by up to 8.7% (for well-to-wheels) and 11.5% (for pump-to-wheels), respectively. Overall, the most beneficial application appeared to be long-haul trucking based on the long distances covered and higher fuel economy achieved (derived from economies of scale), while refuse trucks appeared to have relatively marginal annual savings. However, these annual savings are actually a conservative estimate, which will ultimately be determined by a number of factors that are likely to be predisposed in favor of NG vehicles. Significantly, the prospect of using RNG as fuel was found to be a factor for improving the value proposition of refuse trucks in particular, certainly from an emissions standpoint with a reduction of up to 100%, but speculatively from operational savings as well.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».