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Triple Active Bridge Topology Selection and Design for Integrated On-Board Chargers

2025· article· W4416962232 on OpenAlexaff
Wesam Taha, Sreejith Chakkalakkal, Kyle Kozielski, Amrutha K. Haridas, Kamal Vaghasiya, Gauravkumar Prajapati, Yicheng Wang, Aniket Anand, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcMaster UniversityMcMaster University Medical CentreCanadian Institute for Advanced Research
Fundersnot available
KeywordsNetwork topologyTransformerGalvanic isolationTopology (electrical circuits)Half bridgeBridge (graph theory)ConvertersSelection (genetic algorithm)

Abstract

fetched live from OpenAlex

This paper presents a detailed analysis of topology selection and design for a triple active bridge (TAB) converter tailored for integrated on-board charger (iOBC) applications in electric vehicles (EVs). The TAB converter is pivotal in seamlessly integrating high- and low-voltage batteries while ensuring galvanic isolation. Four distinct topologies are examined with respect to the input source at the secondary and tertiary ports: voltage-fed, current-fed, and a hybrid approach combining both. Comparative analysis identifies the superior performance of the all-voltage-fed topology, featuring a cascaded buck converter at the low-voltage terminal, which leads to the development and prototyping of a three-port transformer for this configuration. Experimental validation confirms its efficiency, demonstrating its suitability for iOBC implementations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.267
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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