A Systematic Design Method for Wireless Power Transfer Systems Using the High-Order Filter Theory
Bibliographic record
Abstract
One of the main factors that limit the practical applications of a wireless power transfer (WPT) system is its possible low-power transfer efficiency (PTE) against changes in distances and misalignments between the Rx and Tx coils, as well as changes in the loads. Although many methods have been proposed to address the issue, a systematic and optimal design method is still missing and much desirable. This article further extends our previous work that uses the first-order filter design approach to a high-order approach with a multicoil system. We first develop the correspondences between the high-order Chebyshev filter and a multicoil WPT system; we then develop a robust design approach to obtaining the circuit parameters of the WPT systems. Both the simulation and measurement results verify the effectiveness of the proposed design methods. They show that by using the second-order Chebyshev bandpass filter design method that involves a four-coil WPT system, we can achieve the PTE at about 75% within specific ranges of changes in distance, misalignments, and load variations, while by using the third-order Chebyshev bandpass filter design method that involves a six-coil WPT system, we can achieve the PTE at about 80% for within specific ranges of changes in distance, misalignments, and load variation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".