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Enregistrement 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 sur OpenAlexaff
C. P. Aiken, Jessie Harlow, Lauren Thompson, Michael Bauer, Toren Hynes, Xiaowei Ma, J. R. Dahn

Notice bibliographique

RevueECS Meeting Abstracts · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Battery Technologies Research
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésRollover (web design)ElectrolyteDielectric spectroscopyElectrical impedanceMaterials scienceChemistryLithium (medication)IonDrop (telecommunication)Analytical Chemistry (journal)ElectrochemistryElectrical engineeringComputer scienceChromatographyElectrodeEngineering

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,371
Score d'incertitude au seuil0,870

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,250
Écart entre enseignants0,237 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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