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Record W4409018544 · doi:10.4271/2025-01-8372

Study of High-Power and High-Energy Lithium-ion Batteries: From Parameter Analysis to Physical Modeling and Experimental Validation

2025· article· en· W4409018544 on OpenAlexaff
Qi Yao, Phillip J. Kollmeyer, Junran Chen, Satyam Panchal, Oliver Groß, Ali Emadi

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLithium (medication)IonEnergy (signal processing)Power (physics)Computer scienceNuclear engineeringMaterials scienceReliability engineeringEngineeringPhysicsThermodynamicsPsychology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This study presents a comparative analysis of Samsung lithium-ion batteries, which are the INR21700 30T high-power (HP) cell and INR21700 50E high-energy (HE) cell, examining their design differences and performance characteristics. Based on teardown data reported in literature, the HP cell features higher porosity, thicker current collectors, and thinner electrode coatings compared to the HE cell, while the HE cell incorporates approximately 6% silicon oxide in its graphite anode for increased energy density. Cell-level characterization test results demonstrated superior rate capability of the HP cell, maintaining 93.8% of its capacity at 2C discharge, while the HE cell retained 93.4% at 1.6C. The HP cell also exhibited better cycle life stability due to its silicon-free design. Pseudo-two-dimensional (P2D) models were constructed using both teardown experimental parameters and adjusted parameters. Simulation results revealed significant discrepancies using teardown parameters, particularly at high C-rates, with HP cell capacity overestimated by 6.22% at 2C and HE cell capacity underestimated by 12.38% at 1.6C. After parameter adjustment, simulation accuracy improved to within ±2% for most conditions. However, these required adjustments highlight limitations of the P2D model to fully represent actual physical processes using experimentally measured parameters.</div></div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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.

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

Citations16
Published2025
Admission routes1
Has abstractyes

Explore more

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