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Record W4384162056 · doi:10.1109/tie.2023.3292879

A Novel Capacitor Voltage-Balancing Method for Multiwinding Resonant Converters

2023· article· en· W4384162056 on OpenAlexafffund
Pengfei Zheng, Cun Wang, Jennifer Bauman

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitorConvertersNetwork topologyElectromagnetic coilElectronic engineeringVoltageDecoupling capacitorEngineeringReliability (semiconductor)Topology (electrical circuits)Computer scienceElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Isolated dc/dc converters play a key role in electric vehicle applications, and novel multiwinding resonant converters have recently been proposed for on-board and off-board charging applications. Some of these topologies have two series capacitors at the dc bus with a mid-point connection to the windings. Parameter mismatch due to manufacturing differences will cause voltage unbalance on these capacitors, which can lead to safety and reliability concerns. However, the well-known capacitor balancing strategies proposed for other topologies are not applicable to these new resonant multiwinding topologies. Thus, this article proposes a novel capacitor voltage-balancing method for a resonant multiwinding converter. The method uses a simple table to decide on balancing switching states. Time-domain analysis is performed to quantify the control cycles required, and an adaptive control strategy is proposed. Experimental results with multiple bus capacitor sizes validate the effectiveness of the proposed method. Furthermore, use of the proposed method is found to slightly increase efficiency.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.982
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.273
Teacher spread0.236 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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