Multi-band Adaptive Impedance Compensation Methods for Spatially Robust Capacitive Power Transfer Systems
Bibliographic record
Abstract
Our paper compares the merits of using both adaptive load resistance and adaptive frequency for impedance compensation of large spatial variations in capacitive power transfer (CPT) systems. We show through an equivalent circuit model how changes to load impedance or changes to frequency can be used to maintain a constant input impedance to the CPT link as the separation distance between the transmit and receive electrodes is varied. In previous works, frequency compensation required continuous adjustment to track spatial changes, thereby requiring broad operating bands for broad spatial ranges. Here, we combine discrete frequency compensation with continuous impedance compensation to keep transmission frequencies within specific narrowband ISM channels. This work presents a practical implementation of spatially robust CPT systems. We consider the limitations of band-limited wireless power channels and apply frequency compensation as discrete frequency changes that are switched between different bands. In this way, frequency compensation offers coarse impedance control for large spatial changes. We then combine the band switching with load impedance compensation methods that offer fine impedance control. Experimental results verify the design concepts using an electrode configuration that is compatible with a differential six-plate CPT system. The results include measurements of the compensated CPT link in isolation as well as an evaluation of compensation applied to an end-to-end system with a class-E amplifier and class-E rectifier. The results show that multi-band operation significantly extends the spatial robustness of the system. Compared to single-band operation, there is a 50\% increase in the compensation gap range in the dual-band system. The choice between multi-band and single-band systems depends on application requirements: multi-band adaptive load compensation works well in systems with large spatial variations, while single-band suffices for applications with modest spatial variation and defined range constraints.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".