Proceedings of Materials for Sustainable Development Conference (MAT-SUS) (NFM22)
DOI: https://doi.org/10.29363/nanoge.nfm.2022.152
Publication date: 11th July 2022
In this lecture I discuss a digital twin devoted to the accelerated optimization of the manufacturing process of Lithium Ion Batteries (LIBs). This digital tool is developed by us within the context of the ERC-funded ARTISTIC project.1 It is supported on a hybrid approach encompassing experimental characterizations, a physics-based multiscale modeling workflow and machine learning models.2 Different steps along the LIB cells manufacturing process are simulated, such as the electrode slurry, coating, drying, calendering and electrolyte infiltration. The physics-based modeling workflow encompasses a sequential coupling between Coarse Grained Molecular Dynamics, Discrete Element Method and Lattice Boltzmann Method. It allows predicting the impact of the process parameters on the final electrode microstructure in three dimensions. The predicted electrode microstructures are injected in a performance simulator capturing the influence of the pore networks and spatial location of carbon-binder within the electrodes on the electrochemical response. Machine learning models are used to accelerate the physical models’ parameterization, to mimic their working principles and to unravel manufacturing parameters interdependencies from the physical models’ predictions and experimental data, and for inverse design. The predictive and optimization capabilities of this digital twin, coupling physical models with machine learning models, are illustrated with results for different electrode formulations in the context of LIBs. I also demonstrate the applicability of our approach to Sodium Ion and Solid State Batteries. Finally, the free online battery manufacturing simulation services offered by the project3 to optimize battery electrodes are illustrated through several examples.
A.A.F. acknowledges the European Union’s Horizon 2020 research and innovation program for the funding support through the European Research Council (grant agreement 772873, “ARTISTIC” project). A.A.F. acknowledges the Institut Universitaire de France for the support.
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