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Record W2279756945 · doi:10.1109/iecon.2015.7392426

A bipolar-DC-bus EV fast charging station with intrinsic DC-bus voltages equalization and minimized voltage ripples

2015· article· en· W2279756945 on OpenAlexaff
Longcheng Tan, Bin Wu, Sebastian Rivera

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectrical engineeringTransformerConvertersVoltageElectric vehicleForward converterComputer scienceElectronic engineeringEngineeringPhysicsBoost converterPower (physics)

Abstract

fetched live from OpenAlex

This paper proposes a bipolar-dc-bus Electric Vehicle (EV) charging station without a line-frequency transformer at the medium-voltage grid side. It is mainly composed of the Neutral Point Clamped (NPC) converter based central ac-dc station and the parallel isolated three-level dc-dc converter based fast chargers. Due to the elimination of the line-frequency transformer, the volume and cost of the system are reduced. Meanwhile, the isolation and step-down between high dc-bus voltages and low load voltages are performed through the high-frequency transformers, leading to increased voltage utilization of switching devices. In order to guarantee the bipolar dc buses balance and minimize the dc-bus voltage ripples, the 180°-interleaved operation for the two parallel isolated three-level dc-dc converters is proposed to make the total neutral-point current zero so that all fast chargers do not bring any unbalance problems. The proposed 180°-interleaved operating principle has been analyzed in detail, then simulation results are presented to verify its performance.

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: none
Teacher disagreement score0.646
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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