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Record W4391636449 · doi:10.1149/1945-7111/ad27b6

Impact of Electrolyte Volume on the Cycling Performance and Impedance Growth of 18650 Li-ion Cells

2024· article· en· W4391636449 on OpenAlexaff
Arman Bonakdarpour, Ivan Stoševski, Aryan Tiwari, Scott R. Smith, Brian Way, David P. Wilkinson

Bibliographic record

VenueJournal of The Electrochemical Society · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsE-One Moli Energy (Canada)University of British Columbia
Fundersnot available
KeywordsCyclingElectrolyteVolume (thermodynamics)Materials scienceIonChemical engineeringChemistryThermodynamicsElectrodeEngineeringHistoryOrganic chemistryArchaeology

Abstract

fetched live from OpenAlex

We provide compelling evidence that the cycling performance of 18650 Li-ion cells is adversely affected by excessive amounts of electrolyte volume, with a noticeable decline observed within the initial 30 cycles, particularly at higher discharge rates. This “high-volume effect” imposes additional constraints on the optimization of cell manufacturing, highlighting the importance of identifying its underlying causes. The electrochemical impedance of 3.5 Ah 18650 cylindrical cells with varying levels of electrolyte volume was extensively measured using PEIS and GEIS techniques. The results indicate that, in general, the ohmic and charge transfer resistance(s) of the cells increase at a faster rate when excess electrolyte volume (9% and 18%) is present. During high discharge rate cycling, relaxation periods can effectively recover the lost capacity, but when high discharge rate cycling resumes, the trend in the capacity loss reappears. We hypothesize that a salt segregation effect in the electrolyte may contribute to the growth of both ohmic and charge transfer resistance, leading to capacity loss when excess electrolyte is present.

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 score0.047
Threshold uncertainty score0.497

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 integrity0.0000.001
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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

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