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Enregistrement W4386855483 · doi:10.1149/ma2023-012539mtgabs

Dry Battery Electrode Manufacturing Enabled By Continuous Powder Mixing

2023· article· en· W4386855483 sur OpenAlexaff
K. Huber, Stefan Stojcevic, Michael O. Wolf, Arno Kwade

Notice bibliographique

RevueECS Meeting Abstracts · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Battery Technologies Research
Établissements canadiensInstitute of Particle Physics
Organismes subventionnairesnon disponible
Mots-clésBattery (electricity)CoatingMaterials scienceSlurryProcess engineeringWaste managementEnvironmental scienceChemical engineeringNanotechnologyComposite materialEngineering

Résumé

récupéré en direct d'OpenAlex

Lithium-Ion Battery electrode manufacturing is a cost- and energy-intensive process that usually relies on the use of a hazardous and expensive solvent, N-methyl-2-pyrrolidone (NMP), for cathodes. After coating the battery-slurry to a current collector, the solvent needs to be evaporated to obtain a porous electrode suitable for the use in a Lithium-Ion Battery. The solvent is a processing material and unwanted in the final product. In fact, solvent residues can fuel parasitic side-reactions within the battery cell and hence deteriorate battery lifetime and safety. The utilization of NMP in battery production plants demands costly labor protection and explosion safety measurements during mixing and coating and necessitates a highly energy and floor-space demanding drying step using thermic drying ovens of up to 100 meters. [1] For ecological and economic reasons, NMP is not emitted in large-scale production facilities, but condensed and recycled by distillation. The NMP-recovery system adds additional floor space and energy demand to the production site. The drying and solvent recovery can account for up to 39% of the total energy demand of a Lithium-Ion Battery Cell production and therefore produce significant CO 2 emissions. [2] A solvent-free, dry electrode manufacturing that eliminates the use of solvents hence reduces the floor space, energy demand and cost of a production plant and eases safety and environmental concerns. Different approaches have been reported in the literature to put dry coating into practice, [3],[4] yet most strategies, for example spray coating or brush coating, lack of a feasible implementation into large scale production. In contrast, a dry coating approach that is sometimes referred to as the Maxwell-Process is, according to media reports, currently installed at Tesla’s Gigafactories in Berlin and Austin. [5] In fact, a publicly available teardown of a Tesla 4680 battery cell hints that a dry coated anode could already be used in commercial vehicles. [6] Compared to the state-of-the-art electrode manufacturing process, dry coating requires different polymeric binder systems that form spiderweb-like structures of fine fibrils connecting the electrochemical active particles and the conductive additive particles. PTFE is highly suitable since it is stable towards typical electrolytes and cathode materials and is known to easily form fibrils. These properties are also utilized to produce expanded PTFE membranes like Gore-Tex. The fibril-network in the battery context is primarily formed by a high-shear dry mixing process. The powder mixture is then compressed to a free-standing electrode film (powder-to-film) by a heated rolling mill (calender) and the resulting film is ultimately laminated to a current collector foil (film-to-foil). We demonstrate that a twin screw-extruder can be used to tune the degree of binder-fibrillation during dry-mixing of NMC-based cathode mixtures. Extrusion based mixing allows to use powder mixtures with little amounts of PTFE binder (1 wt%) to produce self-supporting, free-standing cathode films and, ultimately, battery electrodes with high flexibility and sufficient mechanical stability. We show a superior rate-performance and cycling-stability of single-layer pouch cells with dry-coated cathodes compared to cells with wet coated reference electrodes of likewise composition and electrode design. Possible causes for the improved performance focusing on microstructural electrode properties are discussed. [1] Westphal, Bastian G.; Kwade, Arno (2018): Critical electrode properties and drying conditions causing component segregation in graphitic anodes for lithium-ion batteries. In: Journal of Energy Storage 18, S. 509–517. [2] Erik Emilsson, Lisbeth Dahllöf (2019): Lithium-Ion Vehicle Battery Production. IVL Swedish Environmental Research Institute. [3] Duffner, Fabian; Kronemeyer, Niklas; Tübke, Jens; Leker, Jens; Winter, Martin; Schmuch, Richard (2021): Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure. In: Nat Energy 6 (2), S. 123–134. [4] Verdier, Nina; Foran, Gabrielle; Lepage, David; Prébé, Arnaud; Aymé-Perrot, David; Dollé, Mickaël (2021): Challenges in Solvent-Free Methods for Manufacturing Electrodes and Electrolytes for Lithium-Based Batteries. In: Polymers 13 (3). [5] https://www.tagesspiegel.de/berlin/tesla-coup-mit-giga-berlin-neue-technologie-soll-wasserverbrauch-minimieren/27244032.html [6] https://www.youtube.com/watch?v=8WPPBhqeekw 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 candidatesMéta-épidémiologie (sens strict)
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 enseignants0,253
Score d'incertitude au seuil1,000

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 recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,011
Tête enseignante GPT0,239
Écart entre enseignants0,228 · 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.

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

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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