Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

The optimization of the electrodes manufacturing process is critical to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. LIB electrode manufacturing is a complex process involving multiple steps and parameters. We have shown in our previous works that 3D-resolved physics-based models constitute very useful tools to provide insights into 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 these models. In this work, we tackle this issue by proposing a generalizable and 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 allows to sufficiently represent the manufacturing parameters space. Secondly, the generated dataset is used to train deterministic ML models to implement 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, all being parameters that affect the Li+ (de-)intercalation kinetics, ionic, and electronic transport properties of the electrode.