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Design and Optimization of A High Power Density Low Voltage DC-DC Converter for Electric Vehicles

2020· article· en· W3069000565 on OpenAlexaff
Yang Chen, Wenbo Liu, Andrew Yurek, Xiang Zhou, Bo Sheng, Yan‐Fei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsTransformerInductorElectrical engineeringElectromagnetic coilPower densityVoltageBoost converterForward converterLow voltageHigh voltageEngineeringElectronic engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper presents a high power density LLC converter for Electric Vehicles (EVs) on-board low voltage DC-DC converter. The design specification imposes critical challenges on size and efficiency due to extremely high load current rating and wide input/output voltage range. The proposed design enables high switching frequency by using wide-band-gap (WBG) devices to significantly reduce the size of magnetic components and meet the power density requirement. A two-transformer configuration with series connected primary windings and parallel connected secondary windings is used to reduce the heavy I2R loss on the output side. The structures of parallel resonant inductor and transformers are carefully designed to reduce the fringing loss and AC conduction loss. A single phase 1.3kW LLC prototype with water cooling was built and experiment results verified the design considerations. The prototype achieved 3kW/L of power density and 97% peak efficiency. Full input voltage range from 250V to 430V and output voltage from 9V to 16V operation was verified with 96.5% efficiency achieved at nominal input and full load.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.472

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 head0.188
Teacher spread0.179 · 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
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

Citations10
Published2020
Admission routes1
Has abstractyes

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