9:10 AM - *CT05.11.05
Combining Machine Learning and Multiscale Modeling for Accelerated Battery Manufacturing Optimization
Université de Picardie Jules Verne1,Réseau du Stockage Electrochimique de l'Energie (RS2E)2,Institut Universitaire de France3
Lithium ion batteries (LIBs) are playing a crucial role in the ongoing energy transition, in particular through the renewed emergence of electric vehicles. However, the increasing climate change requires us to develop innovative approaches to accelerate the optimization of LIBs. In this lecture i will present an innovative hybrid computational approach, combining machine learning (ML) and multi-scale modeling (MSM), allowing to predict the impact of manufacturing parameters on lithium ion battery (LIB) electrode properties. Manufacturing parameters include electrode slurry composition and solid to liquid ratio, coating speed, slurry drying temperature, calendering pressure, temperature and rolls speed. Resulting electrode properties include mesostructure information (spatial organization of active and inactive material particles, particles percolation, tortuosity factors, porosity, etc.) and electrochemical performance indicators (overpotentials and specific capacities upon galvanostatic discharge and charge). The ML techniques are used for predictive classification and regression, based on in house experimental databases, and for the acceleration of the parameterization of the physical-based models within the MSM workflow. The overall approach is developed in the context of the ARTISTIC project  and allows performing both direct and inverse design of the optimal manufacturing conditions maximizing given electrode descriptors such as energy and power density. Concrete demonstration examples will be provided on the basis of LIB electrodes made of graphite and Nickel-Manganese-Cobalt active materials, illustrating the strong capabilities of the approach to accelerate the optimization of LIB manufacturing processes.
 ERC Consolidator Project ARTISTIC (Advanced and Reusable Theory for the In Silico-optimization of composite electrode fabrication processes for rechargeable battery Technologies with Innovative Chemistries) (https://www.u-picardie.fr/erc-artistic/).
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