MétaCan
Menu
Back to cohort

A Bidirectional Synchronous Buck/Boost Converter Design for Hybrid Energy Storage System Topologies Optimized for EMC and Power Integrity

2024· article· en· W4400946132 on OpenAlexaff
Manraj Singh Ladhar, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNetwork topologyPower (physics)Energy storageComputer scienceElectronic engineeringBuck converterElectrical engineeringTopology (electrical circuits)EngineeringPhysicsVoltageComputer network

Abstract

fetched live from OpenAlex

A bidirectional synchronous buck-boost converter design is implemented in hybrid energy storage system active topology using the LT8228 controller. Further, peak pulse current tests are conducted in an experimental setup of hybrid energy storage system passive topology using supercapacitors as buffer energy storage pack and a li-ion battery pack. Comparative analysis of both the topologies is presented along with control strategies for optimal power flow management in active topology for enhanced performance and cycle life of primary energy storage li-ion battery pack during high c-rate charging and discharging. This paper also describes good design rules and practices that need to be followed during the layout process of switching regulators for optimal EMI/EMC performance and power integrity in PCBs. Using good transmission line design techniques and power delivery system (PDS) design, a robust PCB design of the DC/DC converter with EMI compliance and power integrity is achieved.

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 score0.011

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Battery Technologies ResearchFrench-language works237,207