Surrogate Modeling of Lithium‐Ion Battery Electrode Manufacturing by Combining Physics‐Based Simulation and Deep Learning

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Abstract
Optimizing the manufacturing process of Lithium‐Ion Batteries (LIB. Finding efficient approaches that accelerate and replace time‐consuming, material scrap‐expensive trials‐and‐error optimization methods is a key area of research. This work presents a comprehensive LIB electrode manufacturing framework that combines physics‐based simulations with deep learning. This framework efficiently simulates the manufacturing process of LIB electrodes as a function of their formulation. This framework takes the form of a surrogate manufacturing model able to predict the impact of manufacturing parameters on the electrode microstructure and properties. The model is based on a regressor‐inspired variational autoencoder method. The analysis of the data and the predicted electrode functional metrics demonstrates the consistency of the approach with an electrode manufacturing model developed on the basis of physics. The reported framework holds significant promise in paving near real time optimization of LIB electrode manufacturing and supporting the optimization of battery cell design in pilot lines.

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