MétaCan
Menu
Back to cohort
Record W2786015346 · doi:10.1109/tpel.2018.2798636

Control and Analysis of a Modular Bridge for Battery Cell Voltage Balancing

2018· article· en· W2786015346 on OpenAlexaff
Atrin Tavakoli, S. Ali Khajehoddin, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsModular designTransformerVoltageElectrical engineeringBattery (electricity)EngineeringElectromagnetic coilHigh voltageComputer sciencePower (physics)Physics

Abstract

fetched live from OpenAlex

A new distributed control scheme and charge flow analysis is presented for voltage balancing of series connected battery cells using nondissipative modular power electronics. Each modular bridge is connected across two battery cells using a high frequency transformer and an asymmetrical half-bridge. This results in using one switch and one diode per battery cell. Intrabridge charge transfer equalizes the voltage of two battery cells within a module using coupled transformer windings. Each modular bridge is connected to adjacent bridges by connecting transformer windings within each module. This allows interbridge charge transfer and the balancing of pairs of battery cells, both within adjacent bridge modules and modules more removed. The proposed controller uses a distributed control strategy whereby the control of each modular bridge monitors its own battery cell voltages and also those of adjacent bridges, thus reducing the number of feedback sensors. Detailed analysis is presented that quantifies the flow of charge between a number of series connected battery cells (N battery cells). A per-unit design methodology is used to illustrate the system charge flow characteristics. Simulations, design guidelines and experimental results are presented to validate the proposed method.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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.

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

Citations28
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207