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Record W2944573793 · doi:10.1109/jestpe.2019.2914706

A Series Resonant Energy Storage Cell Voltage Balancing Circuit

2019· article· en· W2944573793 on OpenAlexafffund
Yanqi Yu, Raed Saasaa, Ashraf Ali Khan, Wilson Eberle

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoltageCapacitorEnergy storageElectrical engineeringSeries and parallel circuitsTransformerEquivalent circuitEqualization (audio)SupercapacitorElectronic engineeringMaterials scienceComputer scienceEngineeringPhysicsCapacitanceElectrodePower (physics)

Abstract

fetched live from OpenAlex

A novel cell voltage equalizer using a series LC resonant converter is proposed for series-connected energy storage devices, namely, battery or super (or ultra)-capacitor cells. The proposed circuit is an active voltage equalization circuit for energy storage devices that is low cost, small in size, and equalizes the voltages quickly. Compared to the state-of-the-art solutions, the proposed series LC resonant circuit eliminates the complexity of multiwinding transformers, and it can balance series-connected energy storage devices in a short time by transporting energy successively between the cells having highest voltage difference. The detailed circuit operation and theoretical analysis are provided. Simulation and experimental results are presented to demonstrate the voltage equalization process from an initial voltage difference of 527 mV to a final difference of 10 mV in 900 s for three series-connected supercapacitor cells.

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 score0.006
Threshold uncertainty score0.021

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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

Citations63
Published2019
Admission routes2
Has abstractyes

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Same venueIEEE Journal of Emerging and Selected Topics in Power ElectronicsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207