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Record W3155847069 · doi:10.1109/tpel.2021.3074378

Soft-Switched Single Inductor Single Stage Multiport Bidirectional Power Converter for Hybrid Energy Systems

2021· article· en· W3155847069 on OpenAlexaff
Rasoul Faraji, Lei Ding, Morteza Esteki, Neda Mazloum, S. Ali Khajehoddin

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science Foundation
KeywordsInductorTopology (electrical circuits)Power (physics)Electronic engineeringBuck converterBoost converterEnergy storageEngineeringVoltageElectrical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

A soft-switched nonisolated multiport bidirectional converter is proposed for hybrid energy system applications. The proposed topology improves the efficiency and expands the applications of the conventional three-port converter (TPC) by adding a soft-switching cell and a bidirectional power flow path from output to charge the energy storage device. Moreover, soft-switching conditions for all TPC switches in all operating modes are achieved. The topology uses one inductor that is shared by all power flow paths. Thus, power conversion in all operating modes is done in a single-stage and the conduction loss is reduced. To implement the soft-switching cell, coupled inductors are used to optimize the magnetic core and volume of the converter. Various converter operating modes are presented, and design considerations are discussed. Moreover, different control system operating modes are explained and designed in detail. Finally, a converter prototype to supply a 250 W–200 V load is implemented, and the theoretical analysis is validated by the experimental results.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.212
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations68
Published2021
Admission routes1
Has abstractyes

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