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Record W4381619339 · doi:10.11159/ffhmt23.164

Comparison of Hybrid Cooling Concepts for Electric Battery Packs

2023· article· en· W4381619339 on OpenAlexaffvenue
Seham Shahid, Martin Agelin‐Chaab

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBattery (electricity)Automotive engineeringComputer scienceElectrical engineeringEngineeringPower (physics)PhysicsThermodynamics

Abstract

fetched live from OpenAlex

In this paper, a hybrid thermal management strategy is proposed to increase the temperature uniformity and improve cooling within a Lithium-ion battery pack.Three different configurations are developed and compared.In all three configurations, the phase change material is placed directly with the cells, and liquid channels are placed in the battery pack to extract heat from the phase change materials.Furthermore, an air duct is placed at the top of the battery to extract heat from the fluids that are stationary within the liquid channels.In the first configuration, the liquid channels are placed in between the phase change material.In the second configuration, the location of the liquid channel is changed such that a part of it is in direct contact with the cells.This configuration is then modified for the third configuration such that for every three cells, there are two liquid channels.Transient numerical studies were conducted, and the results indicated that through the first configuration, the maximum temperature was limited to 31.5 C and the temperature uniformity to 1.75 C.Moreover, this strategy does not require excessive pumping fluid power and high air velocities, which implies that less energy is required for the operation of the thermal management system.

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 score0.337
Threshold uncertainty score0.484

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 head0.033
GPT teacher head0.283
Teacher spread0.250 · 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
Published2023
Admission routes2
Has abstractyes

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