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Record W4416513119 · doi:10.1109/tpel.2025.3636008

Active Damping of <i>LC</i> Interphase Dynamics in Automotive 48-V Series-Capacitor Buck Converters

2025· article· W4416513119 on OpenAlexafffund
W. L. Jiang, J. Pigott, Henk Jan Bergveld, Olivier Trescases

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Language
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersAutomotive industryBuck converterInductorTransient (computer programming)VoltageControl theory (sociology)Transient responseSettling time

Abstract

fetched live from OpenAlex

This paper presents a 4:1 Series-Capacitor Buck (SCB) converter designed for automotive 48-V-to-0.8V applications, targeting improved transient response under rapid input voltage variations. The proposed design addresses the inherent interphase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> oscillations in hybrid dc-dc converters through a novel dynamic flying-capacitor voltage tracking technique. Unlike conventional methods, this approach employs dual flyingcapacitor voltage regulators to dynamically adjust per-phase current commands, ensuring accurate current balancing and robust stability across a wide operating range. A comprehensive small-signal model is developed and analysed to accurately capture and optimise the converter dynamics. Simulations indicate significant reductions in peak inductor currents during input transients. Experimental validation using a 62.5-A, 200-kHz prototype demonstrates a 4× reduction in flying-capacitor voltage settling time to 600 μs under fast input voltage transients. The proposed solution meets strict automotive reliability and performance requirements under rapid input-transient conditions, demonstrating improved overall transient response and enhanced system robustness.

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
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 score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
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 head0.004
GPT teacher head0.226
Teacher spread0.222 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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