Technical and Economic Feasibility of Electrified Highways for Heavy-Duty Electric Trucks
Bibliographic record
Abstract
New solutions to decarbonisation in the transport sector are prominently required to replace oil consumption.Full battery electric vehicles (BEVs) are usually limited to light-duty vehicles due to their energy density.The aim in this study is to evaluate the technical and economic feasibility of electric trucks that are supplemented by electrified highways (eHighways), instead of using conventional diesel, petrol, and full BEVs.The battery is the most expensive component of electric vehicles, especially for heavy-duty trucks.The principle of eHighway is that electricity is supplied to electric vehicles directly from the electric grid as they travel along the road.The eHighway concept is being developed with two primary methods to connect the roadway to the vehicle: conductive power transfer (CPT) where electric connection to the vehicle can be provided from above or below the vehicle and inductive power transfer that is in-motion wireless power transfer (WPT).If eHighways are installed on the major links that connect main cities, the eHighway technology can be suitable for long-distance journeys.This research evaluates the eHighway technologies of both CPT and in-motion WPT.A case study has been conducted.Various costs are calculated and analyzed.Results show that the driving cost (or selling price) of a heavy-duty electric truck on the eHighways using CPT technology ranges from $0.21 to 0.67 per km with varying daily traffic volume.The driving cost of a heavy-duty electric truck on the eHighways using in-motion WPT technology ranges from $0.22-1.03depending on daily traffic volume.If fuel and vehicle prices evolve as predicted between now and 2050, eHighways could become an economically feasible form of road transport, especially for heavy-duty trucks, resulting in energy savings and thus reductions in CO2 emission.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".