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Record W2346936248 · doi:10.1149/ma2016-03/2/823

Ultra High-Precision Studies of Degradation Mechanisms in Aged LiCoO<sub>2</sub>/Graphite Li-Ion Cells

2016· article· en· W2346936248 on OpenAlexaffabout
Reza Fathi, John C. Burns, David A. Stevens, Hui Ye, Chao Hu, Gaurav Jain, Erik J. Scott, Craig Schmidt, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteFaraday efficiencyElectrodeDissolutionIonInterphaseCapacity lossDegradation (telecommunications)Depth of dischargeChemistryMaterials scienceNanotechnologyChemical engineeringBattery (electricity)Electrical engineeringPhysicsEngineeringPhysical chemistry

Abstract

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The major causes of capacity loss in most Li-ion cells are the parasitic reactions between the charged electrodes and the electrolyte. Examples of such reactions are the formation of the solid electrolyte interphase (SEI) at the negative electrode [1, 2], electrolyte oxidation at positive electrode [3], and loss of active materials due to metal dissolution [4, 5]. It is important to study Li-ion cells throughout their lifetime to be able to determine which failure mechanisms dominate so that appropriate changes to cell chemistry and cell design can be made to increase cell lifetime. Measuring coulombic efficiency (CE) using an ultra-high precision charger (UHPC) [6, 7] along with dVdQ analysis [8, 9] are powerful techniques to learn about capacity loss mechanisms of Li-ion cells. This study will show results of Li-ion cells of exactly the same chemistry that were made recently, as well as 3 (G2), 8 (G3) and 12 (G4) years ago. The aged cells were subjected to controlled cycle testing or used in human implants in the field for the majority of their life (at 37oC). This set of cells, obtained from Medtronic, exhibit exceedingly stable performance over a long lifetime and represent an excellent opportunity to learn about aging in Li-ion cells. The cells were charged and discharged between 3.4 V and 4.075 V at Medtronic. then were shipped to Dalhousie University for further studies. The charge current was C/6 and the discharge was either C/24 (G2) or C/150 (G3 &G4). The average discharge capacity losses for cells in groups G2, G3 and G4were ~12.3%, ~19.5% and ~21% of initial capacity after ~500 (3 years), ~435 (8 years), and ~700 cycles (12 years) respectively. Figure 1 shows the fractional discharge capacity data versus time for two cells from each group versus calendar time (a) and cycle number (b). The data indicates that calendar time and cycle # are both contributors to capacity loss; however, calendar fade is clearly the dominant factor. In Figure 1(a), the fractional capacity versus time curves for each of the three groups have been fit (see the solid green lines in Figure 1a) using the expression: q(t) = 1 – A t1/2 [1] where q(t) is the fractional capacity at time, t. A is a fitting parameter which has units of yr-1/2. The t1/2 relationship given in equation 1 is characteristic of capacity loss due to SEI growth on the negative electrode where the fade rate slows down as the SEI film thickens with time following a parabolic growth model [1, 2]. The reasonably good fit in Figure 1(a) is consistent with capacity fade that is dominated by SEI formation. The data for group G2 clearly fades more rapidly than groups G3 and G4, as reflected by the larger value of A needed to fit the data. This is likely caused by a greater number of cycles during the same time for the group G2cells, possibly leading to more expansions and contractions of the graphite particles and more rapid SEI growth. With ageing, the loss of lithium to the SEI continually reduces, leading to more consistent performance with ageing. However, UHPC results, obtained at Dalhousie University, show that parasitic reactions are still occurring in these cells and CE never reaches 1.0000. References : M. Broussely, S. Herreyre, P. Biensan, P. Kasztejna, K. Nechev, R.J. Staniewicz, J. Power Sources., 97, 13 (2001) A. J. Smith, J. C. Burns, Xuemei Zhao, Deijun Xiong, and J. R. Dahn, J. Electrochem. Soc., 158, A447 (2011) K. Xue, Chem. Rev., 104, 4303 (2004). J. Christensen, and J. Newman, J. Elechtrochem. Soc., 150, A1416 (2003). Y. Talyosef, B. Markovsky, G. Salitra, D. Aurbach, H.-J. Kim, S. Choi, J Power Source.,146, 664 (2005). A. J. Smith, J. C. Burns, S. Trussler, and J. R. Dahn, J. Electrochem. Soc., 157, A196 (2010). T. M. Bond, J. C. Burns, D. A. Stevens, H. M. Dahn, and J. R. Dahn, J Electrochem. Soc., 160, A531 (2013). Hannah M. Dahn, A. J. Smith, J. C. Burns, D. A. Stevens, and J. R. Dahn, J. Electrochem. Soc, 159, A1405 (2012). I. Bloom, A. N. Jansen, D. P. Abraham, J. Knuth, S. A. Jones, V. S. Battaglia, G. L. Henriksen, J. Power. Sources., 139, 295 (2005). Figure 1

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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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.259
Teacher spread0.238 · 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".

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Citations0
Published2016
Admission routes2
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