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Record W7002063142

MODELING THE POTENTIAL IMPACTS OF ELECTRIFYING LONG-HAUL TRUCKS IN ONTARIO, CANADA

2024· dissertation· en· W7002063142 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2024
Typedissertation
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsElectrificationTruckGreenhouse gasGridLicenseSuiteRange (aeronautics)Climate changeRouting (electronic design automation)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.032
GPT teacher head0.268
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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