Exploring the Impact of Roadway Materials on Dynamic Electric Vehicle Charging Based on Capacitive Power Transfer
Bibliographic record
Abstract
Wireless power transfer (WPT) opens avenues for contactless charging of various electronic devices. In particular, resonant capacitive power transfer (RCPT) can enable dynamic wireless charging of electric vehicles (EVs); however, the impact of roadway materials on RCPT systems has only recently begun being explored. This paper investigates the performance of CPT-based EV charging through an asphalt-based roadway, i.e., a lossy dielectric. More specifically, for the application where parallel-plate capacitor assumptions are held, it is shown that power transfer efficiency largely depends on the relative permittivity, including loss tangent, thickness of the dielectric layer, and loaded quality factor while being independent of both frequency and plate area. For more realistic RCPT scenarios with significantly lower coupling factors, system efficiency depends on the separation between the transmit resonator and dielectric layer with a tradeoff between coupling factor and dielectric loss. Moreover, with frequency dependent loss tangents, certain frequencies allow for higher efficiencies; the asphalt sample simulated in this study performed better at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6. 7 8 ~ M H z}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 3. 5 6 ~ M H z}$</tex> than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 ~ M H z}$</tex>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".