The Impact of Intentionally Added Water to the Electrolyte in Li-Ion Cells
Notice bibliographique
Résumé
Electrolyte formulation is crucial to extending the lifetime and increasing the performance of lithium ion cells. Keeping moisture levels low (< 15 ppm [1]) has always been a standard in commercial lithium ion cell production which also increases the cost of electrolyte and cell manufacturing. The purpose of this study is to show the impact of small amounts of intentionally added water (100 – 2000 ppm) to the electrolyte impacts cell performance. Recently it has been shown that short term measurements of the coulombic efficiency and charge endpoint slippage rate coupled with storage and impedance measurements can give good indications of long term cycling performance [2-4]. In this study, wound LiCoO2/graphite, Li[Ni0.42Mn0.42Co0.16]O2/graphite and LiCoO2/Li4Ti5O12cells were made (similar to those in references 2-4) and filled with electrolytes containing additives such as vinylene carbonate [3, 5] and LiTFSI [4, 6] with and without intentionally added water to the electrolyte. Duplicate cells were made for both cycling on the High Precision Charger [7] and storage on an automated cycling/storage system [8] both built at Dalhousie University. Along with these short term measurements, impedance spectra were collected and long term cycling performance was evaluated. This presentation will discuss the impact of adding water with and without the presence of these additives in different cell chemistries along with other studies done on pouch cells containing intentionally added water in the electrolyte. One outcome of this work is that there appears to be no negative impact, and perhaps a positive impact in adding 1000 ppm water to LCO-graphite cells in the presence of the additives, VC and LiTFSI. This suggests that in the presence of these additives, water content specifications could perhaps be relaxed somewhat leading to an avenue for cost reduction. Long term cycling results on many of the cells are available to compare to the short-term, precision, measurements. Figure 1. A summary of data collected for LiCoO2/graphite cells, with and without both additives and water in the electrolyte, including charge transfer resistance (Rct), voltage drop during storage (V Drop), coulombic efficiency (shown as 1-CE) and charge endpoint slippage rate (Ch. Slippage). Figure 2. The charge endpoint capacity (top), discharge capacity (middle) and coulombic efficiency (bottom) versus cycle number for LiCoO2/Li4Ti5O12cells at both 30 (left) and 60°C (right) containing various amounts of added water in the electrolyte from 200 – 2000 ppm. References: [1] http://www.targray.com/documents/DMMP-Electrolyte-Solution.pdf, last accessed April 9, 2013. [2] 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 (2011). [3] 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 (2012). [4] J.C. Burns, N.N. Sinha, G. Jain, H. Ye, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J. Choi, and J.R. Dahn, J. Electrochem. Soc., 159, A1095 (2012). [5] B. Simon and J.-P. Boeuve, U.S. Patent No. 5626981 (6 May 1997). [6] M. Armand, M. Gauthier, and D. Muller, U.S. Pat. 5,021,308 (1991) [7] A.J. Smith, J.C. Burns, S. Trussler, and J.R. Dahn, J. Electrochem. Soc., 157, A196 (2010). [8] N.N. Sinha, A.J. Smith, J.C. Burns, G. Jain, K.W. Eberman, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 158 A1194 (2011).
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».