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Record W2987586723 · doi:10.1109/tpel.2019.2950409

A Modular Battery Voltage-Balancing System Using a Series-Connected Topology

2019· article· en· W2987586723 on OpenAlexaff
Atrin Tavakoli, S. Ali Khajehoddin, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsModular designTopology (electrical circuits)VoltageSeries and parallel circuitsTransformerWaveformElectromagnetic coilBattery (electricity)Series (stratigraphy)Computer scienceElectrical engineeringElectronic engineeringEngineeringControl theory (sociology)PhysicsPower (physics)

Abstract

fetched live from OpenAlex

A new modular topology and control method is presented for balancing the voltages of a series-connected string of battery cells. The proposed topology has fewer components compared to similar methods and is characterized as “cell to string to cell” category since charge transfer occurs between all cells during a cycle: this increases the speed of the charge equalization process. Two battery cells are connected to a bridge module and bridge modules are connected in series through transformer tertiary windings. This series connection causes all bridges to have the same effect on each other and makes the system operation and analysis more predictable. As waveforms and transferred charge can be predicted accurately through analysis, the equalization time is also optimized through a design procedure. The easy-to-implement distributed controller described requires only one voltage sensor per two battery 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 score0.004
Threshold uncertainty score0.014

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)0.0040.001

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

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