A Link-and-Load Adaptive IC for Co-optimization of Power Delivery and Energy Storage in Voltage-Mode Resonant Inductive Power Receivers
Bibliographic record
Abstract
We present a stand-alone energy-efficient integrated circuit (IC) for adaptive co-optimization of power delivery and energy storage in resonant voltage-mode inductive power receivers. The IC does this by (a) dynamically adjusting the rectifier’s conduction angle to optimize the load seen by the link, thus isolating the link’s efficiency from load variations, and (b) continuously supplying the load while storing/recycling the excess/deficit received energy in/from a storage capacitor. The conduction angle adjustment is done by controlling the current drawn from the LC tank in a voltage-controlled manner, hence is needless of continuous load power monitoring and resonators manipulation (i.e., disrupting resonance). The charging and recycling are done using a buck-boost charger, which is also responsible for isolating the link from the effect of load variations and capacitive charging. Additionally, an automatic calibration circuit is integrated on chip to adapt the receiver’s operation to link variations, such as coil movement or misalignment. The measurement results demonstrate improvements of up to $\mathbf{9 2. 7 0 \%}$ in the overall charging time and $\mathbf{1 2 7 0 \%}$ in stored power, respectively, compared to non-optimized situations.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".