Optimal Deployment of Dynamic Wireless Charging Lanes for Electric Vehicles considering the Battery Charging Rate
Bibliographic record
Abstract
Dynamic wireless charging (DWC) technology enables the charging of electric vehicles (EVs) en route without the need for stopping on long-distance trips. Based on DWC technology, a dynamic wireless charging system (DWCS) concept is proposed to determine the number of DWC lanes and their locations and lengths considering varying battery charging rates. A two-stage approach is proposed to design the optimal DWCS. First, we propose a mixed-integer model with nonlinear constraints to determine the locations and lengths of the charging lanes. This model is further reformulated as a mixed-integer linear problem to make it suitable to solve with off-the-shelf commercial solvers (e.g., Gurobi). Next, we propose a method to obtain an approximately optimal solution for the number of lanes. Then, a numerical example from a freeway in Guangdong Province, China, is investigated to demonstrate the applicability of the proposed model and its effectiveness in reducing the construction costs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".