MétaCan
Menu
Back to cohort
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 OpenAlexaff
Sepideh Afshar, Kirsten Morris, Amir Khajepour

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.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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

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 designSimulation or modeling
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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIFAC-PapersOnLineSame topicAdvanced Battery Technologies ResearchFrench-language works237,207