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Record W2891385036 · doi:10.1109/mele.2018.2849918

Elements of a Robust Battery-Management System: From Fast Characterization to Universality and More

2018· article· en· W2891385036 on OpenAlex
Balakumar Balasingam, Krishna R. Pattipati

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Electrification Magazine · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBattery (electricity)Battery packState of healthVoltageWaveformUniversality (dynamical systems)State of chargeElectrical engineeringComputer scienceReliability engineeringEngineeringAutomotive engineeringElectronic engineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

A battery-management system (BMS) is essential for the safe, reliable, and efficient operation of a battery pack. The BMS uses three noninvasive measurements from the battery-voltage, current, and temperature-to gauge a battery's state of charge (SOC) and state of health (SOH). These estimates are used in BMS functions, such as the generation of optimal charging waveforms, cell balancing, and the activation of safety mechanisms.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.531

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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