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Record W1982510288 · doi:10.1109/iecon.2013.6700241

Comprehensive comparative analysis of piezoelectric energy harvesting circuits for battery charging applications

2013· article· en· W1982510288 on OpenAlexaff
Tasneem Rumman Huq, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnergy harvestingVoltageBattery (electricity)Electronic circuitHarmonicsElectrical engineeringCantileverPower (physics)Constant power circuitElectronic engineeringPiezoelectricityEngineeringComputer sciencePower factorPhysics

Abstract

fetched live from OpenAlex

In this paper, three different ac-dc converter circuits are compared that can be used as energy harvesting circuits from piezoelectric sources, like periodic mechanical vibration of piezo-material cantilever beam, and used to supply voltage to charge battery energy storage devices. The study in particular investigates the effect of introducing the third and fifth harmonics along with the fundamental input on the output voltage, current and power delivered to the load. The model used for the Li-ion battery cell is also included. The comparison is done on an even basis by all circuits being open looped and having the same input parameters and load characteristics, i.e., battery model, for each circuit. The simulations were conducted using PSIM software implementing the three different energy harvesting mechanism their circuitry. The average load voltage and current was observed and the power delivered also recorded and compared. Simulation results are presented that output voltage is within 3.5 and 4.2 V. The study facilitates in understanding the nature of voltage and current response to the different approaches to the ac-dc conversion circuitry and provides an insight on the regime to store this energy produced by piezoelectricity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.035
GPT teacher head0.253
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations6
Published2013
Admission routes1
Has abstractyes

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