Combining Physics-Based Modeling and Artificial Intelligence for the Optimization of Battery Manufacturing Processes

Abstract The rapid scaling up and process optimization of manufacturing are essential to keep up with current demand and reduce the cost of lithium-ion batteries. In particular, electrode and cell processing and optimization are key in ensuring reliable lithium-ion batteries. Here, we explain how our hybrid modeling approach, combining physics-based simulations and artificial intelligence, can be used to gain insights into performance-manufacturing relationships and optimize the battery cell manufacturing processes. This hybrid approach is a powerful tool that can simulate the various manufacturing steps from powder to power. With this approach, in combination with the acquisition of battery manufacturing pilot line data, it is possible to perform inverse design of the electrode microstructures and cells for optimal properties. A wide diversity of process parameters is taken into account, such as those in the slurry, its drying, and the resulting electrode calendering, together with the parameters associated with the electrolyte filling, the formation, and the electrochemical operation. We briefly discuss various works done under the umbrella of our ARTISTIC project initiative, demonstrating a comprehensive hybrid approach to pave the way toward digital twins of the manufacturing process of lithium-ion and (also) next-generation battery cells.