Exciter–Quadrature–Repeater Transmitter for Wireless Electric Vehicle Charging With High Lateral Misalignment Tolerance and Low EMF Emission
Bibliographic record
Abstract
In this article, an exciter–quadrature–repeater transmitter pad is proposed in order to achieve high lateral misalignment tolerance and low leakage flux emission in electric vehicle wireless charging applications. In the proposed WPT system, a single half-bridge inverter unit is needed to energize the exciter coil, which is magnetically coupled to two larger quadrature repeater coils. The currents in the two repeater coils naturally adjust based on the lateral misalignment of the receiver pad to reduce the leakage flux density around the charging zone. The system can maintain zero phase shift between excitation voltage and current and achieve constant current charging conditions with fixed compensation capacitors in the ±150-mm lateral misalignment range. In order to verify the attributes of the proposed wireless power transfer (WPT) system, a 3-kW experiment setup with a 200-mm vertical gap was built and compared to a three-coil WPT system, which consists of a small transmitter coil, a single large repeater coil, and a receiver coil. According to the experiment results, even under extreme 150-mm lateral misalignment, the proposed WPT system has 90.7% coil-to-coil transmission efficiency. Furthermore, it achieves a 30% reduction of leakage flux density compared to the three-coil system in the 150-mm lateral misalignment case.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".