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Record W4414015776 · doi:10.11159/eee25.116

Thermal Management in Lithium-Ion Batteries for Heat Distribution and Performance in Series and Parallel Configurations

2025· article· en· W4414015776 on OpenAlexvenueno aff
Jung‐Chang Wang

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersNational Science and Technology CouncilNational Taiwan Ocean University
KeywordsLithium (medication)Series (stratigraphy)Thermal management of electronic devices and systemsIonThermalComputer scienceMaterials scienceNuclear engineeringMechanical engineeringThermodynamicsEngineeringChemistryPhysicsGeology

Abstract

fetched live from OpenAlex

Spontaneous thermal runaway has become a pressing concern in the thermal management of lithium-ion batteries, particularly during high discharge rate operations.This study systematically examines the thermal and electrochemical behaviours of lithium-ion batteries under varying discharge rates.The research is conducted in two phases.Initially, experimental discharge tests were performed to characterize the electrochemical properties and thermal responses at different discharge conditions.Subsequently, a one-dimensional electrochemical model coupled with a three-dimensional heat transfer framework was developed and validated against experimental data using numerical simulation software.The validated model was further employed to analyse the heat distribution of batteries configured in series and parallel.Critical temperature thresholds triggering thermal runaway were identified under various C-rate conditions, providing valuable insights into the thermal control and safety of lithium-ion battery systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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