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Record W2311702316 · doi:10.1149/ma2014-02/5/347

The Effect of High Charge Rate Cycling on Coulombic Efficiency Measurements

2014· article· en· W2311702316 on OpenAlexaffabout
John C. Burns, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFaraday efficiencyWork (physics)ChemistryIonLithium (medication)CyclingAnalytical Chemistry (journal)ThermodynamicsMaterials scienceElectrodePhysicsElectrochemistryPhysical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Recent work has shown good correlation between short term measurements of the coulombic efficiency and long term capacity retention during relatively slow cycling (greater than 20 hours per cycle) and high temperature (above room temperature) [1-3] using the High Precision Charger built at Dalhousie University which is able to measure coulombic efficiency to < 0.01% error [4]. Smith et al. [5] studied how the charge rate impacts the coulombic efficiency in commercial cylindrical cells of different positive electrodes and graphite negative electrodes at different temperatures. This work showed that the departure of the coulombic efficiency from the ideal value of 1.00000 was dependant on the time of one cycle and independent of charge rate for slow rates and elevated temperatures. This work could not investigate the dependence of coulombic efficiency on high charge rates due to limitations of the equipment. Smith et al. [5] showed that as the charge rate of Li-ion cells was increased from ~C/100 to ~C/20, the coulombic efficiency (CE) increased such that (1 – CE) = k*t, where k is a constant that increases with temperature and t is the time of one charge-discharge cycle. Extending this idea to higher cycle rates means that at very high rates the coulombic efficiency approaches 1.00000 since there is very little time per cycle for degradation. However, it is well known that lithium plating can occur at high charge rates with graphite negative electrodes [6, 7] and this would lower the coulombic efficiency. It has been shown that the coulombic efficiency of plating and stripping metallic lithium from graphite electrodes is 0.97-0.98 [8] compared to the coulombic efficiency of intercalating and de-intercalating lithium from a graphite electrode which is > 0.995 [9]. Therefore as the charge rate continues to increase past the onset of lithium plating, the coulombic efficiency should begin to depart further from 1.00000 as a larger fraction of the lithium is involved in plating/stripping instead of intercalation/de-intercalation reactions. Figure 1 shows CE versus charge rate results for two temperatures that shows the time dependent degradation regime at low rates on the left (low rates) and the regime governed by lithium plating on the right (high rates). This study will show results of high charge rate cycling on Li[Ni1/3Mn1/3Co1/3]O2 (NMC)/graphite cells at different temperatures to more thoroughly understand the shape of these curves and the impact of lithium plating on precision measurements of coulombic efficiency and endpoint capacity slippage. This behavior needs to be well understood as many applications for Li-ion batteries, including electrified vehicles, desire high charge rates. References: [1] J.C. Burns, G. Jain, A.J. Smith, K.W. Eberman, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 158, A255-A261 (2011). [2] J.C. Burns, N.N. Sinha, D.J. Coyle, G. Jain, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 159, A85-A90 (2012). [3] A.J. Smith, H.M. Dahn, J.C. Burns, and J.R. Dahn, J. Electrochem. Soc., 159, A705-A710 (2012). [4] HPC [5] A.J. Smith, J.C. Burns, and J.R. Dahn, Electrochem. Solid-State Lett., 13, A177-A179 (2010). [6] S.S. Zhang, J Power Source, 161, 1385-1391 (2006). [7] W. Lu, C.M. Lopez, N. Liu, J.T. Vaughey, A. Jansen, and D.W. Dees, J. Electrochem. Soc., 159, A566-A570 (2012). [8] L.E. Downie, L.J. Krause, J.C. Burns, L.D. Jensen, V.L. Chevrier, and J.R. Dahn, J. Electrochem. Soc., 160, A588-A594 (2013). [9] A.J. Smith, J.C. Burns, X. Zhao, D. Xiong, and J.R. Dahn, J. Electrochem. Soc., 158, A447-A452 (2011).

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)0.002
metaresearch head score (Gemma)0.008
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.401
Teacher spread0.327 · 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
Published2014
Admission routes2
Has abstractyes

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