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Record W4414575446 · doi:10.1177/00219983251377212

Advanced thermal management in Li-ion batteries using composite enclosures with embedded thermal bridges

2025· article· en· W4414575446 on OpenAlexaff
Thamasha Samarasinghe, Mihalis Kazilas, Stuart Lewis

Bibliographic record

VenueJournal of Composite Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsThermal management of electronic devices and systemsComposite numberThermalBattery (electricity)CopperReduction (mathematics)Heat generationElectrical conductor

Abstract

fetched live from OpenAlex

The increasing global demand for electric and hybrid vehicles calls for improved thermal management solutions for lithium-ion (Li-ion) batteries, essential to ensuring performance, safety, and lifespan. This study explores the design of composite enclosures with embedded copper thermal bridges as a passive thermal management solution for Li-ion battery modules. Through comprehensive three-dimensional computational fluid dynamics (CFD) simulations and experimental validation, the study examines the impact of cell configurations and inter-cell spacing on temperature distribution. Custom composite enclosures embedded with copper pins demonstrated enhanced heat dissipation, achieving up to a 16.62% reduction in surface temperature. These findings validate the potential of composite materials as a lightweight and efficient alternative to traditional metal casings, offering a safer and more effective thermal management solution aligned with the demands of electric vehicle applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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