Optimal Configuration of Dynamic Wireless Charging Infrastructure for Ehighway Applications
Bibliographic record
Abstract
On-road or dynamic wireless charging systems constitute electrified highways on which electricity from the electric grid is supplied to electric vehicles wirelessly as they travel along the road, rather than the vehicles solely relying on the storage capacity of batteries.Electrification of highways can contribute to decarbonization in the transport sector and provide a solution to range anxiety, high battery costs and long charging times of electric vehicles.However, installing the wireless charging infrastructure along highways is costly.This paper presents a modeling approach that has been developed based on key variables of dynamic wireless charging systems to minimize the infrastructure cost so that the deployment of electrified highways could be economically viable.The overall investment for the dynamic wireless charging systems consists of different types of costs, including those for inverters, road-embedded power transmitter devices, control devices and grid connections.The costs of the different components depend on traffic flows but to different extents, resulting from the amount of energy demanded in a specific section of the electrified highway (i.e. the traffic flows are section-dependent).It is shown that the charging power level that could vary from 165 kW to 400 kW and road coverage ratio of an electrified highway are interrelated with regard to the economic context.Based on the developed model, the configuration and deployment of a proposed electrified highway in Eastern Canada are designed with an optimal charging power level and road coverage ratio or intermittency, thus achieving the best cost effectiveness.Intermittent electrified highways have the potential to reduce overall investment cost over fully electrified highways.In addition, the cost break-up of various components of the dynamic wireless charging system is estimated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".