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Experimental Comparison of Two Liquid Cooling Methods for Ultrafast Charging Lithium-Ion Battery Modules

2021· article· en· W3191604561 on OpenAlexafffund
Ziyu Zhao, Phillip J. Kollmeyer, Jeremy Lempert, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoolantBattery (electricity)Computer coolingMaterials scienceNuclear engineeringLithium-ion batteryLithium (medication)ThermalElectrical engineeringMechanical engineeringEngineeringThermodynamicsThermal management of electronic devices and systemsPhysics

Abstract

fetched live from OpenAlex

This paper presents a comparative study between active liquid cooling methods for a lithium-ion battery module using experimental techniques. Three Kokam Nickel Manganese Cobalt battery cells connected in parallel were tested in two battery modules, one with liquid coolant flowing along the edge of the module and another with liquid coolant flowing between the cells. For the tests, the modules were placed in a thermal chamber at 20°C and coolant was circulated through the modules with a chiller set to 20°C. Several characterization tests, including thermal resistance tests, fast charging tests up to 5C, and drive cycle tests were designed and performed on the battery module. The temperature response of the two modules was collected with fifteen temperature sensors and the performance of both module types is compared and contrasted.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.406
Teacher spread0.352 · 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 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

Citations4
Published2021
Admission routes2
Has abstractyes

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