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Record W4390075056 · doi:10.1109/tpel.2023.3345663

State of the Art on Power Conditioning for Piezoelectric Energy Harvesters

2023· article· en· W4390075056 on OpenAlexafffund
Sima Ghandi, Mohammad Al Janaideh, Lihong Zhang

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMitacsOcean Frontier InstituteCanada Foundation for Innovation
KeywordsEngineeringElectronic engineeringEnergy harvestingElectronic circuitElectrical engineeringInterface (matter)Microelectromechanical systemsPower (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

In recent years, interface circuits of piezoelectric energy harvesters (PEHs) for power conditioning have been significantly advancing. This article comprehensively reviews various techniques for designing such interface circuits. It first highlights some critical challenges in designing and implementing these systems, including impedance matching, cold startup, system size, and self-supplement capability. And then the article reviews the recent solutions generated by the research community. In particular, we discuss recent progress and advancement on hybrid methods and active methodologies that use combinational components for piezoelectric energy harvesting. In addition, the importance of efficient power management for PEHs is emphasized, especially for microelectromechanical system scale PEHs that demand ultra-low power consumption. Finally the study presents various statistical findings and perspectives that can guide future research and development efforts aimed at enhancing the performance of small-scale autonomous conditioning circuits for PEHs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.216
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2023
Admission routes2
Has abstractyes

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