Design Optimization of Multiple-Layer PSCs With Minimal Losses for Efficient and Robust Inductive Wireless Power Transfer
Bibliographic record
Abstract
Inductive wireless Power Transfer (IPT) is a promising technology for remote powering of a wide variety of applications of electronic devices. To design IPT systems with the highest power transfer efficiency and the maximal robustness to coupling factor variations between transmitter and receiver of printed spiral coils (PSCs), high quality factors (Q-Factor) of the utilized PSCs are required. Designing PSCs with highQ-Factoris limited by the eddy current, the proximity effect, and parasitic losses. In this paper, PSC parasitic losses are carefully analyzed and specific design solutions are proposed. Genetic algorithm optimizations are developed to accommodate the proposed design solutions in minimizing losses. Single and multiple layer variable width PSCs are optimally designed with eddy current and proximity effect losses minimized. The designed PSCs are fabricated and experimental measurements are performed. The validity of the proposed approach to largely improve both IPT efficiency and robustness are confirmed. Using multiple coil layers, the robustness to axial and lateral coupling variations between coils is highly improved. For a triple-layer PSC design case, up to 3.5-fold improved robustness are obtained in reference to conventional IPT systems. Compared with the previous state of the art IPT topologies, the highest Figure-of-Merit value is obtained using the proposed design solutions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".