MODELING THE POTENTIAL IMPACTS OF ELECTRIFYING LONG-HAUL TRUCKS IN ONTARIO, CANADA
Notice bibliographique
Résumé
Road freight electrification has been considered by many as a compelling solution to mitigate the transport sector’s greenhouse gas (GHG) emissions. However, widespread adoption remains challenging, primarily due to range anxiety, which is further magnified by the lack of accessible charging infrastructure, particularly in Canada. This dissertation investigates the potential impacts of developing a regionwide on-route charging network on the daily operations of long-haul electric vehicles (LHEVs) and power grid systems in the province of Ontario, Canada, in 2040. The motivation for this future-focused research is twofold. First, the charging demand of LHEVs in the country is currently non-existent due to the infancy of its market diffusion. However, this is expected to change with the help of multi-million-dollar investments in a suite of policy measures for the transition to zero- emission mobility in Canada. Second, the process of designing and building appropriate fast-charging and grid infrastructures can take years to complete. Not accounting for LHEVs’ charging demand could result in instability and disruption of services in the current grid network, especially during peak periods. The second chapter of this dissertation discusses the development of an archetypal routing network (ARN) for long-haul Class 8 heavy commercial vehicles (HCVs) to identify potential on-route charging locations across the province. The general travel behavior of an HCV is expected to be the same regardless of its powertrain. Therefore, using historical truck GPS data, the typical paths of these trucks, as well as their refueling and resting locations, are analyzed to pinpoint a set of candidate locations likely to house these charging infrastructures. It is found that most of these locations are in proximity to Ontario’s critical road links like Highway 401 and Highway 400, which are part of a major North American freight corridor and connect to Northern Ontario, respectively. Next, Chapter 3 builds upon the work described in Chapter 2 to determine the optimal number of on-route charging locations that can support the projected maximum charging demand of LHEVs in Ontario by 2040. A flow-based path-segment coverage model is proposed, which considers the time when each charging event at each candidate location is likely to occur. Based on current technology, results suggest that almost 90% of trips can reach their destinations without visiting an on-route charging location. However, at least 82 on-route fast-charging locations are required throughout Ontario to support the majority of the remaining LHEV trips. In line with the previous chapter, many of these selected locations are situated in proximity to major highways and are likely to be utilized for at least eight hours a day. In general, more than 46 GW of electricity per day is expected to be used when LHEVs have been adopted on a much larger scale in Ontario by 2040. Chapter 4 extends the research presented in the previous chapter by exploring the impacts of different factors influencing LHEV’s driving range to understand how they affect the development of an optimized on-route charging network for large regions like Ontario. Advancements in battery technology, long-term battery degradation, ancillary energy use, and seasonal variations are accounted for in the optimization simulations. Location-specific factors like varying utilization rates and maximum space capacity constraints are also considered. In contrast with the results estimated in Chapter 3, battery- related factors alone lead to a 9% decrease in LHEV trips completed without recharging and a 65% increase in trips requiring on-route charging. Seasonal variations and ancillary energy use further affect LHEV ranges, with summer and winter reducing ranges by approximately 3% and 8%, respectively, necessitating more charging during these seasons. Consistent with the estimated results presented in the previous two chapters, most optimal charging locations are found along major highways, with a few additional sites to meet seasonal demands. Overall, estimated energy demands suggest the need for substantial infrastructure upgrades, particularly in winter, with a 2040 projected daily demand reaching more than 67 GW. Finally, evidence-based policy guidelines are proposed to help various stakeholders make informed decisions regarding the future development of an on-route fast charging network and its associated potential impact on the electrical grid system in a large region like Ontario. Key guidelines include incentivizing on-site charging facilities, prioritizing critical locations of planned on-route charging networks, investing in electrical grid system upgrades, and engaging different stakeholders through various campaigns.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 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 ».