Optimization of Dynamic Wireless Charging Systems and Economic Feasibility Assessment of Electrified Roads
Bibliographic record
Abstract
<div>Dynamic wireless charging (DWC) systems can make up electrified roads (eRoads) on which electricity from the grid is supplied to electric vehicles (EVs) wirelessly while the EVs travel along the roads. Electrification of roads contributes to decarbonizing the transport sector and offers a strong solution to high battery cost, range anxiety, and long charging times of EVs. However, the DWC eRoads infrastructure is costly. This article presents a model to minimize the infrastructure cost so that the deployment of eRoads can be economically more feasible. The investment for eRoad infrastructure consists of the costs of various components including inverters, road-embedded power transmitter devices, controllers, and grid connections. These costs depend on the traffic flow of EVs. The configuration and deployment strategy of the proposed eRoads in Southeastern Canada are designed with optimized charging power and DWC coverage ratio to attain the best cost-effectiveness. Well-designed intermittent or partial DWC systems are shown to be an effective approach to reducing the overall investment. The economic feasibility of the DWC eRoads is assessed using a levelized cost metric. The results show that the DWC technology is economically viable, particularly for long-haul truck transport. In addition, a sensitivity analysis is conducted to evaluate which parameters have a more significant impact on the economic viability of the DWC eRoads.</div>
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".