The ARTISTIC Online Calculator: Exploring the Impact of Lithium‐Ion Battery Electrode Manufacturing Parameters Interactively Through Your Browser

Lombardo, Teo; Caro, Fernando; Ngandjong, Alain C.; Hook, Jean-Baptiste; Duquesnoy, Marc; Delepine, Jean Charles; Ponchelet, Adrien; Doison, Sylvain; Franco, Alejandro A.

doi:10.1002/batt.202100324

Cited by 19 publications

(26 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approximately 25,000 particles were simulated (6,000 AM and 19,000 CBD). The size of the CBD particles in the dried and calendered electrode was of 2 μm, while it was expanded to account for carbon + binder + solvent at the slurry phase, as in Refs [19][20][21][22]28] . The AM sizes selected to account for the experimental Na 2 BPDC particle size distribution were 1.5, 3.9, 6.95, 13.1, and 22.7 μm, whose frequency were calculated as weighted average of the experimental one (Figure S1).…”

Section: Computational Sectionmentioning

confidence: 99%

Experimentally Validated Three‐Dimensional Modeling of Organic‐Based Sodium‐Ion Battery Electrode Manufacturing

Lombardo

Lambert

Russo

et al. 2022

Batteries & Supercaps

Self Cite

View full text Add to dashboard Cite

Lithium‐ion batteries (LIBs) have triggered the transition from internal combustion engine cars to electric vehicles, and are also making inroads into the grid storage sector, but the future quantity of batteries necessary poses several challenges in terms of raw material availability and sustainability. For this reason, many alternative chemistries are being proposed, such as substituting lithium with other more abundant elements, like sodium, or shifting from inorganic to organic‐based active materials, all of which require the development and testing of new chemistries. The electrode properties are not only a function of the chemistry, but also of the electrode manufacturing and resulting microstructure. In this work, we applied a three‐dimensional computational workflow, initially developed in the context of inorganic‐based electrodes for LIBs, to simulate the manufacturing of sodium‐ion battery anodes using an in‐house synthesized organic‐based active material. This computational workflow accounts for the slurry, its drying, and electrode calendering steps, and was validated by comparing simulated and experimental properties of the slurry and electrode. In addition, the calendering step was studied computationally to identify optimal electrode compressions, and the trend observed was confirmed experimentally through galvanostatic cycling in half‐cell configuration. The positive results shown here are an important demonstration of the chemistry neutrality of our manufacturing models, paving the way towards their application to both commercial and novel chemistries.

show abstract

Section: Computational Sectionmentioning

confidence: 99%

Experimentally Validated Three‐Dimensional Modeling of Organic‐Based Sodium‐Ion Battery Electrode Manufacturing

Lombardo

Lambert

Russo

et al. 2022

Batteries & Supercaps

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ARTISTIC project offers free online services to launch manufacturing simulations from an Internet browser. [ 92,93 ] Other example is the DEFACTO project, [ 94 ] whose target is to create a basis for the digitalization of the battery manufacturing process, by developing multiphysics and multiscale modelling tools to improve the understanding of cell material behaviour and cell manufacturing process, and their impact on battery cell ageing. A further example of a commercial tool that stochastically generates electrode mesostructures is the software GeoDict.…”

Section: Current and Near‐future Developments In Digitalization Of Th...mentioning

confidence: 99%

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

Ayerbe

Berecibar

Clark

et al. 2021

Advanced Energy Materials

Self Cite

View full text Add to dashboard Cite

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and nearterm developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202102696.

show abstract

“…They are being integrated by us in an online calculator, usable through any Internet Browser, allowing to simulate the LIB manufacturing process without the need of computational skill. By April 2022, the first version of this online calculator is being used by 330 researchers and students with 26 % of them coming from the industry [21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

Duquesnoy¹,

Liu²,

Dominguez³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The optimization of the electrodes manufacturing process constitutes one of the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because LIB electrode manufacturing is a complex process involving multiple steps and interdependent parameters, we have shown in our previous works that 3D-resolved physics-based models constitute very useful tools to provide insights about the impact of the manufacturing process parameters on the textural and performance properties of the electrodes. However, their high-throughput application for electrode properties optimization and inverse design of manufacturing parameters is limited due to the high computational cost associated with this kind of model. In this work, we tackle this issue by proposing an innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties and inverse design of its manufacturing process. Firstly, the pipeline generates a synthetic dataset from physics-based simulations with low discrepancy sequences, that allow to sufficiently represent the manufacturing parameters space. Secondly, the generated dataset is used to train deterministic ML models for the implementation of a fast multi-objective optimization, to identify an optimal electrode and the manufacturing parameters to adopt in order to fabricate it. Lastly, this electrode was successfully fabricated experimentally, proving that our modeling pipeline prediction is physical-relevant. Here, we demonstrate our pipeline for the simultaneous minimization of the electrode tortuosity factor and maximization of the effective electronic conductivity, the active surface area, and the density,

show abstract

The ARTISTIC Online Calculator: Exploring the Impact of Lithium‐Ion Battery Electrode Manufacturing Parameters Interactively Through Your Browser

Cited by 19 publications

References 38 publications

Experimentally Validated Three‐Dimensional Modeling of Organic‐Based Sodium‐Ion Battery Electrode Manufacturing

Experimentally Validated Three‐Dimensional Modeling of Organic‐Based Sodium‐Ion Battery Electrode Manufacturing

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

Contact Info

Product

Resources

About