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Enregistrement W3025139641 · doi:10.1149/ma2020-012257mtgabs

Ester and Carbonate-Based Low Temperature Electrolytes in High Specific Energy and High Power 18650 Li-Ion Cells for Future NASA Missions

2020· article· en· W3025139641 sur OpenAlexaff
Marshall C. Smart, Frederick C. Krause, John‐Paul Jones, B. V. Ratnakumar, Mark Shoesmith

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Battery Technologies Research
Établissements canadiensE-One Moli Energy (Canada)
Organismes subventionnairesnon disponible
Mots-clésMars Exploration ProgramElectrolyteMaterials scienceSpecific energyEnergy storageSaturnLithium (medication)Work (physics)Range (aeronautics)Environmental scienceAstrobiologyAerospace engineeringPower (physics)Process engineeringChemical engineeringMechanical engineeringPlanetChemistryPhysicsEngineeringComposite materialElectrodeThermodynamics

Résumé

récupéré en direct d'OpenAlex

NASA continues to have an interest in developing high specific energy and high power rechargeable batteries that can operate well over a wide temperature range. Concepts for applications that could be enabled or enhanced by such technology include: (i) future Mars landers, (ii) future Mars rovers, including a possible Mars Sample Return mission, and (iii) future planetary aerial vehicles, where high specific energy, high power and wide operating temperature range is desired. Future missions to some of the distant icy moons of Jupiter and Saturn are also anticipated to benefit from improved ultra-low temperature rechargeable batteries with high specific energy. 1 To meet these needs, the Electrochemical Technologies Group (ETG) at the Jet Propulsion Laboratory (JPL) has developed a number of low temperature Li-ion electrolytes utilizing various approaches. In general, the performance targets of this work is to provide operation over the temperature range of +40 o C to -60 o C (delivering up to 150 Wh/kg at -40 o C at reasonable rates). In addition, continuous operation at low temperatures is desired, so the cells should possess good charge characteristics without undesirable lithium plating. In previous collaborative work with E-One Moli Energy Ltd. 1 , we have demonstrated excellent specific energy at -40 o C (> 150 Wh/kg) at low rates (C/100) in custom 18650-sized Li-ion cells containing JPL- developed electrolytes. The electrolytes investigated included all-carbonate-based low EC-content electrolyte formulations, as well as solutions containing ester co-solvents with various additives. 2-6 In an extention of this work, we have investigated the performance of a number of Li-ion electrolytes optimized for low temperature performance in custom high specific energy cells as well high power prototype 18650-size cells manufactured by E-One Moli. The electrolytes evaluated included blends which contain elements of various approaches, including (i) ester co-solvents (such as methyl propionate, methyl butyrate, and propyl butyrate), (ii) the use of electrolyte additives (such as VC and FEC), and (ii) the use of mixed lithium electrolyte salts. In contrast to the previous work that was focused on low rate operation at high temperature, emphasis was placed on characterizing the cells using more aggressive discharge rates over a range of temperatures. To evaluate the high specific energy and high power 18650-size cells, extensive discharge rate characterization was performed over a wide temperature range (down to -80 o C). Emphasis was also devoted to establishing the charge acceptance characteristics of the cells at very low temperatures, especially at -40 o C. Given that lithium plating when charging at low temperatures is a known degradation mode of Li-ion cells in general, attention was focused upon characterizing the conditions in which its likelihood may be more pronounced and attempting to detect its occurrence indirectly. These results will be compared to baseline commercial off the shelf (COTS) cells. DC current interrupt impedance measurements have also been performed as a function of temperature in an attempt to more fully understand the impact of electrolyte type upon the low temperature performance for the cells. ACKNOWLEDGEMENT The work described here was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA) and supported by an internal JPL Research and Technology Development (R&TD) Fund. The information in this document is pre-decisional and is provided for planning and discussion only. 1. M. C. Smart, F. C. Krause, J. –P. Jones, L. D. Whitcanack, , B. V. Ratnakumar, E. J. Brandon, and M. Shoesmith, 2016 Prime Pacific Rim Meeting on Electrochemical and Solid-State Science, Honolulu, HI, October 2-7, 2016. 2. M. C. Smart, B. V. Ratnakumar, K. B. Chin, and L. D. Whitcanack, J. Electrochem. Soc. , 157(12) , A1361-A1374 (2010). 3. M. C. Smart, B. L. Lucht, S. Dalavi, F. C. Krause, and B. V. Ratnakumar, J. Electrochem. Soc., 159 (6), A739-A751 (2012). 4. M. C. Smart, B. V. Ratnakumar, F. C. Krause, L. D. Whitcanack, E. A. Dewell, S. F. Dawson, R. B. Shaw, S. Santee, F. J. Puglia, A. Buonanno, C. Deroy, and R. Gitzendanner, NASA Aerospace Battery Workshop, Huntsville, Alabama, November 17-19, 2015. 5. M. C. Smart, et. al., 2010 Power Sources Conference, Las Vegas, NV, June 16, 2010, Pages 191-194. 6. (a) M. C. Smart, et. al., 214 th Meeting of the Electrochemical Society, Honolulu, HI, Oct. 12-17, 2008. (b) M. C. Smart, A. S. Gozdz, L. D. Whitcanack, and B. V. Ratnakumar, 220 th Meeting of the Electrochemical Society, Boston, MA, October 11, 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 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 enseignants0,095
Score d'incertitude au seuil0,956

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 Opus0,007
Tête enseignante GPT0,207
Écart entre enseignants0,200 · 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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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