An Efficient PCB Based Magnetic Coupler Design for Electric Vehicle Wireless Charging
Bibliographic record
Abstract
This paper proposes an efficient Printed Circuit Board (PCB) design of magnetic couplers for electric vehicle (EV) wireless power transfer (WPT) system. Since a WPT system operates at high frequency, Litz wire typically is used that can overcome high AC resistance, and improve overall efficiency. In EV wireless charging applications, the power level of the system is high, and therefore the magnetic pad size and copper cross-section of the Litz wire used in the magnetic couplers are large which increases the cost and weight. Besides, building the magnetic couplers using Litz wire requires excessive labor work, which causes fabrication errors. Replacing the Litz wire with PCB-based coils raises design and efficiency challenges. A new approach for designing the PCB-based magnetic couplers for high-power EV wireless charging applications is proposed to address the challenges associated with the PCB designs. In the proposed design, the efficiency of the PCB-based design is close to the Litz wire-based design. Moreover, machine assembly replaces the labor work and the magnetic coupler can be implemented with lower fabrication error, weight, and manufacturing cost. In this paper, a 3.3kW PCB-based WPT system is designed, compared experimentally with the Litz wire setup which achieved a competitive efficiency profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".