Digitally Controlled Energy Harvesting Power Management System
Bibliographic record
Abstract
Intermittent energy harvesting devices often have difficulties in harvesting the peak energy generated due to battery power limitations, which increase the size and cost of the device. This paper discusses a power electronics module (PEM) that is used to extract power from a human energy harvesting device according to the user's desired difficulty level while maximizing energy transfer into a battery. The PEM can temporarily store the peak power produced by the generator, allowing a reduction in the battery size required to regulate the average power produced by the harvester. A two-stage prototype (a digitally controlled average current mode boost converter and an average current mode buck converter) has been designed, and the experimental waveforms were captured to validate the control theories used in the PEM. The peak efficiencies of the boost and buck are measured to be 93% and 93.7%, respectively. The total PEM system efficiency is measured at 87.9% at an average input power level of 10 W. The PEM design was able to extract 50% more power than the single-stage converter without energy storage capability. The PEM is also used to demonstrate the flexible resistance control scheme capabilities of the device for broader usage in bioenergy harvesting research.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".