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Record W2766633029 · doi:10.1016/j.ifacol.2017.08.269

State of Charge estimation via extended Kalman filter designed for electrochemical equations

2017· article· en· W2766633029 on OpenAlex

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

VenueIFAC-PapersOnLine · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExtended Kalman filterKalman filterState of chargeObserver (physics)Control theory (sociology)Battery (electricity)Alpha beta filterAlgebraic equationInvariant extended Kalman filterComputer scienceState (computer science)ElectrochemistryEnsemble Kalman filterControl engineeringApplied mathematicsMoving horizon estimationEngineeringAlgorithmMathematicsChemistryPhysicsElectrodeNonlinear systemPhysical chemistryControl (management)Thermodynamics

Abstract

fetched live from OpenAlex

Lithium ion batteries are used to store energy in electric vehicles. Physical models based on electro-chemistry are partial differential equations coupled to algebraic equations. The state of the battery, and most importantly, the state of charge (SOC) needs to be estimated using limited measurements. In this paper, an extended Kalman filter (EKF) is developed using the electro-chemical model. Some simplifications to the full electro-chemical model are made to facilitate the estimation. The performance of the observer is demonstrated through comparison of simulation results with experimental data.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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