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Record W2976098016 · doi:10.3390/en12193721

Extreme Fast Charging Technology—Prospects to Enhance Sustainable Electric Transportation

2019· article· en· W2976098016 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTransformerElectrical engineeringAutomotive engineeringElectronicsBattery (electricity)EngineeringComputer scienceVoltagePower (physics)

Abstract

fetched live from OpenAlex

With the growing fleet of a new generation electric vehicles (EVs), it is essential to develop an adequate high power charging infrastructure that can mimic conventional gasoline fuel stations. Therefore, much research attention must be focused on the development of off-board DC fast chargers which can quickly replenish the charge in an EV battery. However, use of the service transformer in the existing fast charging architecture adds to the system cost, size and complicates the installation process while directly connected to medium-voltage (MV) line. With continual improvements in power electronics and magnetics, solid state transformer (SST) technology can be adopted to enhance power density and efficiency of the system. This paper aims to review the current state of the art architectures and challenges of fast charging infrastructure using SST technology while directly connected to the MV line. Finally, this paper discusses technical considerations, challenges and introduces future research possibilities.

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.

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 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: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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