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Record W2964907157 · doi:10.1109/isie.2019.8781250

Design of Variable Inductor for Powertrain DC-DC Converter

2019· article· en· W2964907157 on OpenAlexaff
Mebrahtom Beraki, João Pedro F. Trovão, M. S. Perdigão

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsInductorConvertersElectromagnetic coilPowertrainDC-to-DC converterFinite element methodElectrical engineeringComputer scienceElectronic engineeringControl theory (sociology)Automotive engineeringEngineeringForward converterBoost converterPhysicsTorqueVoltageControl (management)

Abstract

fetched live from OpenAlex

Variable Inductor (VI) uses a small control current to modulate the permeability of a magnetic core and regulate its characteristics. This makes it suitable for applications with a stringent space limitation and a wide range of load variations. Nonetheless, this device is composed of multiple windings and several quantities can be used. Hence, its design is a demanding task and it is even much more complex in case of electric vehicles (EV) converters. This paper provides a systematic design procedure of VI taking into consideration of the nature and requirements of a three-wheel recreational electric vehicle. A VI is designed for a 33 kW nominal and 82 kW peak bidirectional DC-DC converter. Unlike the classical methods, the design procedure is based on the RMS current rather than the peak current. The designed VI is evaluated with FEM simulations. In comparison to the peak current design, the RMS based design and the use of VI resulted in 46% volume reduction.

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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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