Improving Wireless Power Transfer Efficiency Considering Rectifier Input Impedance and Load Quality Factor
Bibliographic record
Abstract
This paper presents a method to maximize power transfer efficiency (PTE) in wireless power transfer (WPT) systems with low-quality factors merely based on tuning the receiver-side components. The paper also suggests an algorithm to find the optimum components values considering the effect of input impedance of receiver-side rectifier at high-frequency (MHz) applications such as portable electronic devices. Existing methods often consider the input impedance of the full-bridge rectifier as a pure resistance at low frequencies. However, at high frequencies, the complex impedance of the rectifier should be taken into account since it impacts on the components optimum values to maximize PTE. Despite conventional maximizing PTE methods for series-series and series-parallel topologies, this paper shows that a series-parallel WPT system with low load quality factor requires adjusting the resonant capacitor in addition to resistive load. The validity of the proposed method is verified based on numerical simulations and experiment tests using a 100 mW cm-scale prototype of a resonant inductive link at 6.78 MHz with varying distance between coils. The test results show a PTE improvement up to 40% in a series-parallel WPT compared with the methods that only tune the load equivalent resistance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".