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Record W2966150139 · doi:10.1109/tpel.2019.2931739

An Overview and Comprehensive Comparative Evaluation of Current-Fed-Isolated-Bidirectional DC/DC Converter

2019· article· en· W2966150139 on OpenAlexaff
Xuewei Pan, Hongqi Li, Yitao Liu, Tianyang Zhao, Chenchen Ju, Akshay Kumar Rathore

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsNetwork topologyConvertersRenewable energyPhotovoltaic systemElectronic engineeringDual (grammatical number)Electrical engineeringTopology (electrical circuits)VoltagePower (physics)Modulation (music)EngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Renewable energy generations have been attracting sustained attentions in academic and industry. Current-fed-isolated-bidirectional dc/dc converters (CF-IBDCs) are widely adopted in low-voltage high-current applications such as solar photovoltaic fuel cell with energy storage. This paper gives an overview and a comprehensive comparative evaluation of CF-IBDCs. The active clamped, dual half-bridge, L-L type dual active bridge, resonant-type, naturally clamped, and other type topologies of CF-IBDCs are investigated, analyzed, and compared regarding their circuit topological structures, operation characteristics, modulation methods, and soft-switching technologies. In addition, component cost models and loss models of converters are deduced and presented. On this basis, quantitative and comprehensive performance comparison of seven typical CF-IBDC topologies selected from each type is conducted in terms of costs, losses, weight, volume, and power density for the given system specifications and design constraints. Finally, the application range of different CF-IBDCs and future trend are presented to encourage further development.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.321
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations199
Published2019
Admission routes1
Has abstractyes

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