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Record W2038198328 · doi:10.4271/2012-01-0334

Temperature Rise in Prismatic Polymer Lithium-Ion Batteries: An Analytic Approach

2012· article· en· W2038198328 on OpenAlexaff
Peyman Taheri, Majid Bahrami

Bibliographic record

VenueSAE International journal of passenger cars. Electronic and electrical systems · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLithium (medication)IonMaterials sciencePolymerChemical engineeringEngineering physicsComposite materialChemistryPhysicsOrganic chemistryEngineeringPsychology

Abstract

fetched live from OpenAlex

A rigorous three-dimensional analytical model is proposed to investigate thermal response of batteries to transient heat generation during their operation. The modeling is based on integral-transform technique that gives a closed-form solution for the fundamental problem of heat conduction in battery cores with orthotropic thermal conductivities. The method is examined to describe spatial and temporal temperature evolution in a sample prismatic lithium-ion battery (EiG ePLB C020), subjected to transient heat generation in its bulk, and various convective cooling boundary conditions at its surfaces (the most practical case is considered, when surrounding medium is at a constant ambient temperature). The full-field solutions take the form of a rapidly converging triple infinite sum whose leading terms provide a very simple and accurate approximation of the battery thermal behavior. A surface-averaged Biot number has been proposed that can simplify the thermal solutions under certain conditions. The presented analytical model provides a fast yet accurate tool for battery thermal management system designs.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations78
Published2012
Admission routes1
Has abstractyes

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Same venueSAE International journal of passenger cars. Electronic and electrical systemsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207