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Record W2483992065 · doi:10.1109/pedg.2016.7527038

A bi-directional AC-DC converter for electric vehicle with no electrolytic capacitor

2016· article· en· W2483992065 on OpenAlexaff
Behnam Koushki, Praveen Jain, Alireza Bakhshai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrolytic capacitorCapacitorRippleFilm capacitorElectrical engineeringMaterials scienceComputer scienceElectronic engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

An isolated single-stage bi-directional AC-DC converter with no electrolytic capacitor for electric vehicle battery charger application is proposed. The topology utilizes a single-stage series resonant dual Half-bridge AC-DC converter to transfer the power and an active-shunt filter on the DC-side to absorb the 100/120Hz current ripple and prevent it from going to the battery. Diverting the low frequency ripple to the active filter prevents extra heat production in the battery and increases of the lifespan of the battery. Also film capacitors, instead of electrolytic capacitors with a lower lifetime, can be used on the DC-side filter. The reliability of the system is increased by not using an electrolytic capacitor in the circuit. To control the circuit for a wide range of power and power angle under soft switching, a four-part controller is proposed. The controller obtains ZVS for all the active switches while it optimizes the performance of the system in terms of conduction losses. ZVS operation of the circuit, enables the switching frequency to increase without increasing the switching losses. Increased switching frequency will result in smaller reactive components and smaller transformer size. The proposed controller obtains the best performance from the circuit. Simulation results with PSIM and experimental results to verify the theory have been carried out.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.350

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 communication0.0000.000
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 head0.009
GPT teacher head0.223
Teacher spread0.215 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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