Enhanced Piezoelectric Energy Harvesting System Model Leveraging Phase Shifting Effects for Optimization
Bibliographic record
Abstract
Conventional piezoelectric cantilever beams experience limited operating bandwidth and reduced output power due to phase angle changes post-resonance.The phase shifting phenomenon between excitation and responses in piezoelectric cantilever beams is a critical factor in energy conversion efficiency.In this study, an equivalent circuit model for a multi-mode vibration energy harvesting system is presented, incorporating various electrical configurations to improve performance in terms of power output and bandwidth through phase shifting effects.The analytical modeling and experimental results of the proposed configurations demonstrate a significant increase in output power, particularly between intersections of successive peaks when the reverse polarity series connection is incorporated into the system.The phase difference between two cantilever beams is significantly reduced due to polarity changes, thereby boosting output voltage in the suggested configurations.The obtained frequency response output indicates that by alternating the polarities of the series connections in the vibration energy harvester, the output power between the valley of two successive frequencies at resonance for the energy harvesting system can be increased by up to 76 percent.This paper highlights the potential of leveraging phase shifting effects to optimize piezoelectric energy harvesting systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".