Résumé
The manufacturing process of Lithium-ion battery electrodes directly affects the practical properties of the cells, such as their performance, durability, and safety. While computational physics-based modeling has been proven as a very useful method to produce insights on the manufacturing properties interdependencies as well as the formation of electrode microstructures, their high computational costs prevent their direct utilization in electrode optimization loops. In this work, a novel time-dependent deep learning (DL) model of the battery electrodes manufacturing process is reported, demonstrated for calendering of nickel manganese cobalt (NMC111) electrodes, and trained with time-series data arising from physics-based Discrete Element Method (DEM) simulations. The DL model predictions are validated by comparing evaluation metrics (e.g., mean square error (MSE) and R2 score) and electrode functional metrics (contact surface area, porosity, diffusivity, and tortuosity factor), showing very good accuracy with respect to the DEM simulations. The DL model can remarkably capture the elastic recovery of the electrode upon compression (spring-back phenomenon) and the main 3D electrode microstructure features without using the functional descriptors for its training. Furthermore, the DL model has a significantly lower computational cost than the DEM simulations, paving the way toward quasi-real-time optimization loops of the 3D electrode architecture predicting the calendering conditions to adopt in order to obtain the desired electrode performance.