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Record W2946916637 · doi:10.1155/2019/2613893

Onboard Charging DC/DC Converter of Electric Vehicle Based on Synchronous Rectification and Characteristic Analysis

2019· article· en· W2946916637 on OpenAlexvenueno aff
Haijun Tao, Guopeng Zhang, Zheng Zheng

Bibliographic record

VenueJournal of Advanced Transportation · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsnot available
FundersHenan Polytechnic University
KeywordsRectificationCommutationForward converterTransformerElectrical engineeringFlyback converterVoltageĆuk converterDiodeEngineeringBuck–boost converterInductorBoost converter

Abstract

fetched live from OpenAlex

The DC/DC converter is the core part of the two-stage electric vehicle Onboard Charger. At present, the phase-shifted full-bridge soft-switching DC/DC converter has problems such as difficulty in commutation of the lagging leg, voltage fluctuation on the secondary side of the transformer, and low efficiency. A full-bridge DC/DC converter with two clamp diodes and synchronous rectification is proposed in this paper. Clamping diodes are used to suppress the voltage oscillation of the secondary side of the transformer and provide the commutating energy of the lagging leg. Synchronous rectification reduces the loss of the switching device. The operating principle and control method of the DC/DC converter are analyzed, and the switching device loss is calculated. The simulation and experimental results show that, compared with the traditional DC/DC converter, the voltage impulse of the secondary side of the transformer is smaller, the efficiency is higher, and the soft switch can be realized in a wide load range, which satisfies the requirement of fast charging of vehicle-mounted batteries.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.599

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 integrity0.0000.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

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