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Record W4289792359 · doi:10.1109/tpel.2022.3196328

A Dual-Current-Fed Dual-Active-Bridge DC/DC Converter With High-Frequency Current-Ripple-Friendly Ports

2022· article· en· W4289792359 on OpenAlexafffund
Yue Zhang, Li Ding, Nie Hou, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundAlberta Innovates
KeywordsRippleInductorSnubberConvertersCurrent (fluid)VoltageElectrical engineeringBoost converterElectronic engineeringForward converterCapacitorEngineeringComputer science

Abstract

fetched live from OpenAlex

The current-fed isolated dc/dc converters are suitable solutions for applications requiring low high-frequency current ripple and convenient current control, such as fuel cells and Li-ion batteries. The existing current-fed isolated dc/dc converters are combined with a current-fed bridge-type terminal and a voltage-fed bridge-type terminal, which can only partially obtain the merits of the current-fed terminals. In this article, a dual-current-fed dual-active-bridge dc/dc converter is proposed with the inductor connection on both terminals. Hence, low high-frequency current ripple and convenient current control can be realized on both sides. Besides, it can also realize the voltage spike suppression without snubber circuits and has the inherent soft-switching operation and voltage boost function. Then, the operation principle, design process, and loss analysis of the proposed converter are presented in detail. The experimental results based on a proof-of-concept laboratory prototype are given to verify the steady-state and dynamic performance of the proposed converter.

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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations40
Published2022
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207