Energy Logistics Cost Study for Wireless Charging Transportation Networks
Bibliographic record
Abstract
Many cities around the world encourage the transition to battery-powered vehicles to minimize the carbon footprint of the transportation sector. Deploying large-scale wireless charging infrastructures to charge electric transit buses when loading and unloading passengers have become an effective way to reduce emissions. The standard plug-in electric vehicles have a limited amount of power stored in the battery, resulting in frequent stops to refill the energy. Optimal siting of wireless charging bus stops is essential to reducing these inconveniences and enhancing the sustainability performance of a wireless charging bus fleet. Wireless charging is an innovation of transmitting power through electromagnetic induction to portable electrical devices for energy renewal. Online Electric Vehicle (OLEV) is a new technology that allows the vehicle to be charged while it is in motion, thus removing the need to stop at a charging station. Developed by the Korea Advanced Institute of Science and Technology (KAIST), OLEV picks up electricity from power transmitters buried underground. This paper aims to investigate the cost of the energy logistics for the three types of wireless charging networks: stationary wireless charging (SWC), quasi-dynamic wireless charging (QWC), and dynamic wireless charging (DWC), deployed at stops and size of battery capacity for electric buses, using OLEV technology for a bus service transit in the borough of Manhattan (MN) in New York City (NYC).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".