A Voltage-Feedback-Based Maximum Power Point Tracking Technique for Piezoelectric Energy Harvesting Interface Circuits
Bibliographic record
Abstract
This paper presents and demonstrates a voltage feedback-based technique to implement a power management integrated circuit (PMIC) for piezoelectric energy harvesting. It is analytically shown that the conducting time interval of a rectifying diode at the maximum power point is a fixed ratio of the vibration period. Thus, it can be used as a feedback to track the maximum power without measuring the output current/power. The technique can be tailored to various interface circuits, including full-bridge, voltage doubler, and synchronized switch harvesting on an inductor. The micro-fabricated PMIC includes a full-bridge rectifier, a digital MPPT controller, and a zero-current-switching (ZCS) integrated buck converter that uses two off-chip inductor and rectifying capacitor. The proposed technique enables the implementation of robust and power-efficient PMICs for maximum power point tracking of piezoelectric energy harvesters. To evaluate the performance of the technique, a PMIC using 130 nm CMOS technology is implemented and tested with a low power (<0.5 mW) piezoelectric energy harvester. The results show that the PMIC effectively tracks the maximum power point at different vibration frequencies and amplitudes while the power consumption of its control circuitry is less than 0.001 mW.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".