A Real-Time-Link-Adaptive Operation Scheme for Maximum Energy Storage Efficiency in Resonant CM Wireless Power Receivers
Bibliographic record
Abstract
The development, analysis, and experimental validation of an energy storage algorithmic scheme for performance optimization of resonant inductive power receivers are presented. Motivated by the crucial role of efficient energy storage in the next generation of brain-implantable devices, we introduce an energy management strategy in the design of wireless powering links, in which, the key performance measure is the energy stored during a limited time interval rather than the average energy delivered to the load. The presented strategy is proven analytically to yield the theoretically-maximum energy storage efficiency over a pre-determined period of time. Additionally, thanks to the algorithm's closed-form solution, the optimization can be done in real time, offering the potential for a solution that is adaptive to any variations in physical (e.g., coil separation, Rx rotation, etc.) and/or electrical (e.g., Q-factor, media conductivity, etc.) properties of the link, conditional to a low-power circuit implementation for its evaluation. The efficacy and precision of the solution obtained from the presented analytical model is confirmed with CAD-based simulation results, and later validated using experimental measurements. Our experimental results for two links with different characteristics (resonance frequency, coils size and separation, etc.) show a 52.5% and 67.5% improvement in overall energy storage efficiency compared to the standard CM receiver design in which resonance-to-charging switching is performed when the receiver's LC tank energy accumulation starts to saturate. This is while the presented method does not require any calibration and is designed to be employed by any generic current-mode receiver.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".