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Record W4295168580 · doi:10.14447/jnmes.v25i3.a02

A Single-Input Dual-Output DC-DC Converter for Powertrain of PEM Fuel Cell Vehicle

2022· article· en· W4295168580 on OpenAlexvenueno aff
Mudadla Dhananjaya, Devendra Potnuru, B. Krishna Chaitnya, Naresh Patnana, Jagadish Kumar Bokam

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
Fundersnot available
KeywordsInductorConvertersDuty cycleDual (grammatical number)PowertrainVoltageBuck–boost converterBoost converterBuck converterComputer scienceĆuk converterForward converterControl theory (sociology)Electrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Multi-output converter plays a vital role in portable electronic and electric vehicle (EV) applications. In this regard, a new single-input dual output (SIDO) converter is proposed in this paper. Most of the single-input dual-output converter configurations presented by various researchers in the domain of multi-output converters function under particular assumptions about operational duty cycle and inductor current.Also, the issue of crossregulation is still prevalent while operating the loads in many SIDO converters.The proposed configuration generates two output voltages in boost and buck-boost modes without any constraints on the duty ratioor inductor currents. In addition, it doesn't encounter cross-regulation problems; subsequently, the output voltage V01 (V02) is not influenced by load changes in i02 (i01). To verify the feasibility and effectiveness of the proposed configuration, a 200 W prototype circuit is developed; simulation and experimental results are validated.

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 score0.003
Threshold uncertainty score0.010

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 integrity0.0000.001
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 head0.019
GPT teacher head0.252
Teacher spread0.232 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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