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Optimized Minimum-Loss Hybrid Multiple Phase Shift Modulation Technique for Dual Active Bridge Converters for MEA Applications

2022· article· en· W4310929549 on OpenAlexaff
Jiaqi Yuan, Niloufar Keshmiri, Mohamed Ibrahim, Rachit Pradhan, Ali Emadi

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConvertersModulation (music)Power (physics)Computer scienceVoltageDual (grammatical number)Electronic engineeringControl theory (sociology)Phase (matter)Boundary (topology)Topology (electrical circuits)Three-phaseCurrent (fluid)PhysicsElectrical engineeringMathematicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

This paper reviews zero-voltage switching (ZVS) possibilities with multiple phase shift modulations for the more electric aircraft (MEA). Three additional triple-phase shift modes are proposed and analyzed in detail based on ZVS conditions beyond the traditional six modes. A minimum peak current stress closed-loop hybrid multiple phase shift (HMPS) modulation scheme based on the proposed modes of operation is presented. Multiple phase shift angles are optimized based on accurate pre-calculation of output current, providing the minimum peak current and decreasing the switching loss, enabling the design of power-dense converters. The proposed HMPS modulation shows higher efficiency at light load and boundary operating points than the existing SPS and unified TPS (UTPS) methods. Finally, a 10kW SiC DAB converter hardware setup for the MEA application is implemented. The efficiency of the setup is up to 96.5%. At boundary operating points, the converter obtains a peak efficiency of 94.7% with the proposed HMPS method. The converter shows peak efficiency improvements of 1%-2% compared to traditional modulation.

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)0.001
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 score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.268
Teacher spread0.235 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicAdvanced DC-DC ConvertersFrench-language works237,207