Carbon-Binder Migration: A Three-Dimensional Drying Model for Lithium-ion Battery Electrodes
Teo Lombardo,Teo Lombardo,Alain C. Ngandjong,Alain C. Ngandjong,Amal Belhcen,Amal Belhcen,Alejandro A. Franco +6 more
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TLDR
In this paper, a physics-based three-dimensional model able to mimic additive migration during drying was presented, unlocking the generation of 3D heterogeneous electrode mesostructures.About:
This article is published in Energy Storage Materials.The article was published on 2021-09-12 and is currently open access. It has received 35 citations till now.read more
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Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence
TL;DR: In this article , the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multi-scale battery behavioural models.
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Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities
Franco Martín Zanotto,Diana Zapata Dominguez,Elixabete Ayerbe,Iker Boyano,Christine Burmeister,Marc Duquesnoy,Marlene Eisentraeger,Jonathan Florez Montaño,Alfonso Gallo-Bueno,Lukas Gold,Florian Hall,Nicolaj Kaden,Bernhard Muerkens,Laida Otaegui,Yvan Reynier,Simon B. Stier,Matthias Thomitzek,Artem Turetskyy,Nicolas Vallin,Jacob Wessel,Xukuan Xu,Jeyhun Abbasov,Alejandro A. Franco +22 more
TL;DR: In this paper , the authors propose a roadmap to full integration between pilot lines and a digital twin for lithium ion battery manufacturing, which will be used for optimization and automatization.
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Towards autonomous high-throughput multiscale modelling of battery interfaces
TL;DR: In this article , the authors review the challenges that need to be overcome to understand interfaces in rechargeable batteries and present future directions to reach this goal, and present a review of the current challenges and future directions.
Journal ArticleDOI
Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations
Marc Duquesnoy,Chaoyue Liu,Diana Zapata Dominguez,Vishank Kumar,Elixabete Ayerbe,Alejandro A. Franco +5 more
TL;DR: In this paper , a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of lithium ion battery electrode properties and inverse design of its manufacturing process is proposed. But the authors do not consider the effect of the manufacturing process parameters on the textural and performance properties of the electrodes.
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Deconvoluting the impacts of the active material skeleton and the inactive phase morphology on the performance of lithium ion battery electrodes
TL;DR: In this paper , a 3D modeling approach based on stochastic generation of electrode mesostructures with realistic LiNi1/3Mn 1/3Co/3O2 particle size distributions is proposed.
References
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Electrical Energy Storage for the Grid: A Battery of Choices
TL;DR: The battery systems reviewed here include sodium-sulfur batteries that are commercially available for grid applications, redox-flow batteries that offer low cost, and lithium-ion batteries whose development for commercial electronics and electric vehicles is being applied to grid storage.
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TL;DR: This year, the battery industry celebrated the 25th anniversary of the introduction of the lithium ion rechargeable battery by Sony as discussed by the authors, which used a combination of lower temperature carbons for the negative electrode to prevent solvent degradation and lithium cobalt dioxide modified somewhat from Goodenough's earlier work.
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