Electric Vehicle Charging Station Infrastructure Along the Trans Canada Highway with Solar PV Energy
Bibliographic record
Abstract
Trans Canada Highway (TCH) is Canada’s most important highway for coast to coast travelling which serves economic, tourism, services and many other purposes. With rising emissions cap, Canada aims to fully electrify all new vehicle sales by 2035. This will result in requiring electric vehicle (EV) charging infrastructure with the increased EV. TCH will follow and requires a new charging infrastructure. From National Resources Canada’s database, a total of 346 light duty battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) trims are found from 2024 and 2025 MY, which are expected to be part of the 2035 electric vehicle fleet. From their data, average EV total range is found to be 499.63 ± 113.61 km. Additionally, BEVs have an average battery capacity of 100.09 ± 29.08 kWh, while PHEVs have 17.54 ± 7.22 kWh. Currently, EV only holds 3.89% of the total registered vehicles in Canada but is projected to be 11.33% in 2035. Taking a conservative value of 5% of current annual average daily traffic (AADT) on the TCH to be BEV with BEV battery, this project studies the solar power required to produce the power demand. With battery decay, the paper assumes that every 400km, vehicles need to make a stop, and the power required in that region would be split into 4 charging stations, each only requiring to produce 1/4th the total energy. Using System Advisor Model, a total of 89 charging stations are designed and placed along the TCH, with an average of 81.20 ± 12.60 km orthodromic distance between each other. From solar PV data available, details of the solar plants adjoint with the charging stations are analyzed. The plant sizes are found to range between 300 to 6500 kW due to the varying AADT and solar irradiance throughout the TCH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".