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A Single-Stage AC/DC Bridge-Less Converter with an Adaptive Control Scheme and Reduced DC-Link and Output Capacitances for High Voltage EV Systems

2023· article· en· W4378843567 on OpenAlexaff
Siamak Derakhshan, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsCapacitorBoost converterĆuk converterForward converterCharge pumpControl theory (sociology)Flyback converterCapacitanceBuck converterController (irrigation)VoltageElectronic engineeringBuck–boost converterComputer scienceEngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a new AC/DC step-up CLLC resonant converter with reduced dc-link and output capacitance and an adaptive control scheme for fully soft-switching operation for high-voltage EV systems. In the proposed converter, a closed-loop controller guarantees soft-switching operation and high-quality sinusoidal input current without a need for any auxiliary circuits. Moreover, the designed adaptive control system allows for replacing bulky dc-link and output filter capacitors with small capacitors that result in a high-power density, higher reliability, and reduced size converter system. Moreover, the proposed adaptive control scheme allows the converter to further narrow its switching frequency spectrum for output voltage regulation, resulting in an extended high-efficiency range while maintaining soft-switching operation. Furthermore, a variable frequency control algorithm is designed to regulate the converter's output voltage during its modes of operation. The performance of the proposed converter system is verified through a 2kW, 120Vac/800Vdc design.

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: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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
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

Citations4
Published2023
Admission routes1
Has abstractyes

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