MétaCan
Menu
Back to cohort
Record W4285142492 · doi:10.1109/tpel.2022.3187568

Dual-Inverter-Integrated Three-Phase EV Charger Based on Split-Phase Machine

2022· article· en· W4285142492 on OpenAlexaff
Caniggia Viana, Mehanathan Pathmanathan, Peter W. Lehn

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInverterDrivetrainElectrical engineeringComputer scienceThree-phaseElectronic engineeringElectric vehicleTopology (electrical circuits)EngineeringVoltagePower (physics)TorquePhysics

Abstract

fetched live from OpenAlex

Significant effort has been dedicated to developing integrated onboard charging circuits for electric vehicles, aiming to improve cost, range anxiety, and charging convenience. The dual-inverter drivetrain topology has attracted particular attention as a platform for developing such solutions, being previously leveraged for the implementation of dc and single-phase ac onboard charging. This work proposes an integrated three-phase onboard charger based on the dual-inverter drivetrain. The proposed converter is implemented with minimal change to the dual inverter and no additional power electronics by introducing a split-phase electric machine. A mathematical model is developed, decomposing the system into four decoupled subsystems, individually responsible for charging, driving, grid common-mode current, and zero-sequence current generation, respectively. In light of this model, a novel space-vector pulsewidth modulation technique is introduced to ensure charging current control while generating no flux-producing, nor zero-sequence currents, and having superior common-mode performance. Simulation-based and experimental verification is conducted on a 7.2-kW scaled-down prototype to prove the charging concept, as well as the common-mode current elimination.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207