Wireless Power Transfer Systems Optimization Using Multiple Magnetic Couplings
Bibliographic record
Abstract
Multiple magnetic couplings used to increase the link distance in wireless power transfer systems (WPTSs) are not new. An efficient power transfer in conditions of an extended link distance requires a series connection of the intermediate coils. However, all four connections of the emitter and receiver coils are equally possible. This present paper conducts an extensive analysis of WPTSs utilizing three magnetic couplings. The type of connection of the emitter and receiver coils represented the criterion utilized for the WPTS optimization assessment. The first step requires the determination of the schematic of the sinusoidal equivalent circuit. Then, one synthesizes the functions describing the system performances (e.g., the amount of delivered active power or efficiency) by applying the entirely symbolic and or the hybrid symbolic-numerical formalism. The output of such functions consists of appropriate representation in the frequency domain, based upon Laplace state variable equations (SVE) or complex or Laplace modified nodal equations (MNE). The dependency of the WPTS performance on the number of magnetic couplings and their parameters included a study on resistive loss minimization. The minimization applies to the intermediate coils, whereas the outcomes are the active delivered power and the power transfer efficiency—the first study case aimed at a comparison between two distinct WPTSs: three magnetic couplings versus two. The second case of the study compared the WPTSs having a series connection of three magnetic couplings with those built with the emitter-receiver resonators in parallel. One determined the normalized sensitivities as frequency functions, which depend on circuit resistances, load resistance and the coupling factor between the second and the third coil. The optimization algorithms are suitable for computing optimal parameters of the given circuit to ensure maximum and minimum values of the performance value. Good simulation examples followed the proposed optimization techniques.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".