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Record W2964798962 · doi:10.1109/isie.2019.8781528

EV/HEV Industry Trends of Wide-bandgap Power Semiconductor Devices for Power Electronics Converters

2019· article· en· W2964798962 on OpenAlexaff
Amin Ghazanfari, Christian Perreault, Karim Zaghib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMaterials scienceInsulated-gate bipolar transistorSilicon carbidePower semiconductor deviceWide-bandgap semiconductorGallium nitridePower electronicsOptoelectronicsEngineering physicsSemiconductorElectrical engineeringBand gapPower moduleSilicon bandgap temperature sensorElectronicsVoltagePower (physics)NanotechnologyEngineeringVoltage regulator

Abstract

fetched live from OpenAlex

During the past two decades, medium-power inverters for electric and hybrid electric vehicles (EV/HEV) have been dominated by mature silicon (Si) insulated-gate bipolar transistor (IGBT) technology. New power semiconductor technologies, such as silicon carbide (SiC), have attracted increasing interest as attractive alternatives that address the limitations of the band-gap width, breakdown voltage, saturation drift rate, and thermal conductivity of Si devices. This paper provides an overview of wide-bandgap power semiconductors from the perspectives of the efficiency, reliability, added cost, and market, particularly for EV and charging infrastructure applications. In addition, the potential of wide-bandgap power devices is analyzed, including SiC, gallium nitride (GaN), and synthetic diamond, and the industry vision and evolution related to SiC penetration into the automotive supply chain are described.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.226
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 teacher head, not a consensus.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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