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Record W2025787363 · doi:10.1109/iecon.2013.6700158

Electric vehicle charging station using a neutral point clamped converter with bipolar DC bus and voltage balancing circuit

2013· article· en· W2025787363 on OpenAlexaff
Sebastian Rivera, Bin Wu, Jiacheng Wang, Hussain S. Athab, Samir Kouro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectric vehicleElectrical engineeringAutomotive industryGridAutomotive engineeringComputer sciencePower (physics)VoltageEngineering

Abstract

fetched live from OpenAlex

The automotive industry is making important changes towards the electrified transportation. Several Electric Vehicles (EVs) and Plug-in Hybrid EVs are currently on the market offering a clean and environment friendly alternative to conventional vehicles. However, limited mileage capacity and long refueling times have lead to a lukewarm reception from the consumers. In order to reduce the charging time fast charging architectures are being developed, usually as off-board solutions located in public installations. This paper presents a centralized charging station architecture, using a bipolar dc grid to power off-board chargers and integrate of DGs and storage systems. The central converter topology is an NPC and a novel balancing technique is implemented to extend the operating range of the power converter.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.556

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 communication0.0000.001
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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.

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

Citations25
Published2013
Admission routes1
Has abstractyes

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