Optimal Design of Fully Integrated Magnetic Structure for Wireless Charging of Electric Vehicles
Bibliographic record
Abstract
Optimal design of a fully integrated magnetic structure for electric vehicle (EV) wireless charging is proposed in this article. The proposed approach helps save ferrite material, reduce the implementation cost, shrink the size of the converter, and improve the cost and power density. Typically, in a wireless charging system, anLCCresonant network and the receiver dc–dc converter are used, which require a bulky inductor in their structures. The integration of resonant inductor with the transmitter-side coil has been studied in the literature to reduce the overall cost and improve the power density of an EV wireless charger. The integration of the dc–dc inductor with the vehicle-side receiver coil is also introduced in the literature. However, the full integration of both resonant inductor and dc–dc inductor into the transmitter and receiver coils of the wireless charging system creates new design challenges that have not been addressed before. In this article, the proposed fully integrated magnetic structure and its design challenges are studied in detail. A procedure for achieving the optimal design of the fully integrated structure is presented. An optimization problem is defined to design the best magnetic structure and select the best values for the resonant elements and dc–dc inductor. The outcome of this integration is an all-integrated magnetic structure, compact converter, and efficient wireless charger. A 2.2-kW/85-kHz system is built to verify the feasibility of the proposed integrated wireless charging system, and its performance is evaluated through the experiment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".