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Study on Temperature Uniformity and Rising Rate of High-Power Lithium-Ion Battery Pack

2014· article· en· W1987869713 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsOperating temperatureBattery packBattery (electricity)Nuclear engineeringPower (physics)Materials scienceVolumetric flow rateAutomotive engineeringElectrical engineeringEnvironmental scienceEngineeringThermodynamics

Abstract

fetched live from OpenAlex

According to the operating characteristics of high-power battery pack in practical application, the effects of four factors, which include operating load, state of charge (SOC), ambient temperature and operating temperature, on the temperature uniformity and rising rate of battery pack under natural convection were studied based on Arbin test bench. The results indicate that: the operating load and SOC of battery pack doesn’t affect its temperature uniformity, which was deteriorated as the increasing of operating temperature of battery pack, or improved as the increasing of ambient temperature when the other factor is kept constant. The increment of operating load or ambient temperature could result in a higher temperature rising rate, while the increasing of SOC or operating temperature could lead to a lower temperature rising rate, when the other three factors are kept constant. When the operating temperature or temperature differences achieved threshold value, thermal management system should choose proper extent of forced flow and flow field in accordance with the battery pack’s current operating conditions, in order to ensure the economy 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.

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)0.001
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 score0.420
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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