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High Efficiency Bidirectional LLC Converter for Solar-Charged Electric Vehicles

2021· article· en· W3193100303 on OpenAlexaff
Pengfei Zheng, Jennifer Bauman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectric vehicleElectrical engineeringGridAutomotive engineeringEngineeringBattery chargerBattery (electricity)Photovoltaic systemVehicle-to-gridSolar powerPower (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Recent interest in electric vehicles with on-board solar generation presents a new opportunity for using solar electric vehicles (SEVs) for distributed generation when the traction battery is fully charged. This presents a new power electronic challenge: the on-board charger should have high efficiency in the grid-to-vehicle direction for normal charging power levels (i.e., 6.6 kW) and also high efficiency in the vehicle-to-grid direction for much lower solar power levels (i.e., <800 W). However, typical on-board charger topologies have low efficiency at light loads. This paper proposes a novel bidirectional LLC converter to address this challenge. PLECS simulation results show that the proposed converter is 1–7% more efficient than the traditional dual bridge converter at low loads for vehicle-to-grid power flow.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.591

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 head0.007
GPT teacher head0.203
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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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