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Record W4386855483 · doi:10.1149/ma2023-012539mtgabs

Dry Battery Electrode Manufacturing Enabled By Continuous Powder Mixing

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

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsBattery (electricity)CoatingMaterials scienceSlurryProcess engineeringWaste managementEnvironmental scienceChemical engineeringNanotechnologyComposite materialEngineering

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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".

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Citations2
Published2023
Admission routes1
Has abstractyes

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