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Record W7009481798

Electrochemical-thermal Analysis of High Capacity Li-ion Pouch Cell for Automotive Applications

2020· dissertation· en· W7009481798 on OpenAlexfundno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2020
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersPolitecnico di TorinoUniversity of Windsor
KeywordsAutomotive industryBattery (electricity)ThermalHeat generationVoltageAutomotive engineLimit (mathematics)Fossil fuel
DOInot available

Abstract

fetched live from OpenAlex

Latest regulatory trends implemented in order to limit emissions combined with research advances in alternative fuels have paved the road toward vehicle electrification. Major original equipment manufacturers (\\acrshort{OEM}s) have already marketed electric vehicles in large scale but apart from business strategies and policies, the real engineering problems must be addressed. Lithium-ion batteries are a promising technology for energy storage; however, their low energy density and complex electro-chemical nature, compared to fossil fuels, presents additional challenges. Their complex nature and strong temperature dependence during operation must be studied with additional accuracy, capable to predict their behavior. In this research, a pseudo two dimensional (\\acrshort{P2D}) electro-chemical model, coupled with a 3D thermal energy balance for a recent high capacity \\acrshort{NMC} pouch cell for automotive applications is developed. The electrochemical model with its temperature dependent parameters is validated at different temperatures and various discharge C-rates to accurately replicate the battery cell operational conditions. The sources of heat are distinguished and characterized via advanced electrochemical-modelling approach, in various battery operations and different thermal boundary conditions. For example, it was determined that the temperature rise during discharge at high C-rates, under natural convection, could result in thermal runaway, if managed incorrectly. Ohmic heat generation of current collectors and cell tabs is investigated and included. Hence, the thermal analysis provides insights on the current and voltage profiles causing the minimum thermal stress on the cell and the location of heat generation spatially and temporally during the battery discharge. Different modelling approximation of the cell are studied starting from the cell fundamental unit. This provides effective design considerations for the battery thermal management system (\\acrshort{BTMS}) to enhance performance, cycle life and safety of future electrified vehicle energy storage 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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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