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Record W2934895474 · doi:10.1149/ma2019-03/2/223

Using Varied Salt Concentration and High Charging Potential to Study “Rollover” Failure Mechanisms in Li-Ion Cells

2019· article· en· W2934895474 on OpenAlexaff
C. P. Aiken, Jessie Harlow, Lauren Thompson, Michael Bauer, Toren Hynes, Xiaowei Ma, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRollover (web design)ElectrolyteDielectric spectroscopyElectrical impedanceMaterials scienceChemistryLithium (medication)IonDrop (telecommunication)Analytical Chemistry (journal)ElectrochemistryElectrical engineeringComputer scienceChromatographyElectrodeEngineering

Abstract

fetched live from OpenAlex

After many charge-discharge cycles of showing little to no capacity fade, Li-ion cells can undergo rapid degradation in capacity that occurs over relatively few cycles [1]. We refer to this sudden, accelerated capacity loss as “rollover” failure. “Rollover” failure should be concerning to manufacturers and academics alike, because it can be difficult to predict when it will occur and can require years of cycling to verify. In some instances, “rollover” failure can be caused by impedance growth during cycling that eventually limits the available lithium inventory at a particular charging current due to an ohmic voltage drop. This impedance growth is followed by true loss of lithium inventory by lithium plating. We show that the impedance growth of the cell during cycling is, among many other factors, strongly tied to the concentration of salt used in the electrolyte. Using more salt (up to a reasonable limit) reduces cell impedance at all frequencies, provides better impedance control during cycling and extends the number of cycles until “rollover” failure. Ultra-High Precision Coulometry and Electrochemical Impedance Spectroscopy combined with post failure electrolyte analysis by Li-ion Differential Thermal Analysis, Gas Chromatography Mass Spectrometry and Inductively Couple Plasma Mass Spectrometry provide clues of how the cell and electrolyte change with varying salt concentration, and as the cell fails. Finally, we propose a means of testing to accelerate “rollover” failure. Cycling protocols with long constant voltage segments at the top of charge are shown to accelerate impedance growth and “rollover” failure. Using this cycling protocol on cells with electrolytes containing low salt concentrations can reduce the time to “rollover” to a few months. With traditional cycling and good cells, containing electrolytes with 1M – 1.2M salt concentrations and good electrolyte additives, this can take years. We believe that cycling with long periods at high potential, of cells with low salt concentration electrolytes is an accelerated means of testing to quickly screen electrolyte additives, electrode coatings and other cell material choices. Figure 1 shows the discharge capacity and ΔV (difference between average charge and discharge voltages) versus cycle number of cells the follow our prescribed method to accelerate “rollover”. The cells contained electrolytes with varying salt concentrations and spend 24 hours at 4.4V every second charge-discharge cycle. Figure 1 clearly shows that lifetime is increased with increased LiPF 6 concentration. Similarly, Figure 1 shows that use of the electrolyte additive LiPO 2 F 2 (LFO) extends lifetime when compared to the combination of fluoroethylene carbonate (FEC) and dioxathiolane-2,2-dioxide (DTD). Cells with longer lifetimes show better impedance control, as evidenced by ΔV. Figure 2 shows discharge capacity and ΔV versus cycle numbers for cells that are tested using typical CCCV cycling to 4.3V. The comparison between FEC and DTD versus LFO is the same as in Figure 1, but the data in Figure 2 took 8 months to collect and distinguish the two additive systems. This is eight times longer than it took to distinguish the two additive systems using cells with 0.2 M LiPF 6 that were held at 4.4V for 24h every second charge, as shown in Figure 1. “Rollover” failure can be prevented by ensuring that cell impedance remains constant. Increasing LiPF 6 concentrations appears to control impedance, as does avoiding extended times at high voltage. Doing the opposite results in a high throughput screening method than can quickly distinguish the lifetime benefit of small changes in cell chemistry, like electrolyte additives. [1] J. C. Burns, A. Kassam, N. N. Sinha, L. E. Downie, L. Solnickova, B. M. Way, J. R. Dahn, J. Electrochem. Soc. , 160 , A1451-A1456 (2013). Figure 1

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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 score0.371
Threshold uncertainty score0.870

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 integrity0.0000.000
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 head0.250
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

Citations1
Published2019
Admission routes1
Has abstractyes

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