Computational Analysis of Manufacturing-Electrochemical Interface Linkages in Battery Electrodes

Rechargeable batteries are being transformative for our societies. Their performance and durability are highly dependent on the manufacturing process, which impacts the microstructure and the interfaces between the materials (e.g. active material, carbon additive, binder) in the electrodes. These aspects are strongly governed by the electrode manufacturing parameters, such as the slurry formulation, the slurry mixing (for solvent-based processing), the coating speed and drying rate, the calendering pressure, temperature and speed. Additionally, electrolyte filling conditions are important (in the case of lithium ion batteries for instance).<br/>In this lecture, I present my group's latest computational research in assessing the manufacturing-electrochemical interface linkages in battery electrodes. I report the latest developments of our dynamic 3D-resolved digital models able to predict how manufacturing parameters impact the microstructure of electrodes used in lithium ion and solid state battery cells. Such models, describing the different steps along the manufacturing process and calibrated with experimental data from our battery manufacturing pilot line, are supported on a sequential coupling of computational granular approaches like Coarse Grained Molecular Dynamics and Discrete Element Method. The electrode microstructures predicted by these models are injected into simulators of the electrolyte filling and the electrochemical performance, by using the Lattice Boltzmann Method and the Finite Element Method respectively. The latter simulates dynamically and in 3D the electrochemical and transport processes in the electrodes and captures at the mesoscale, the influence of manufacturing parameters on the spatiotemporal heterogeneities of lithiation/delithiation. Deep learning is also applied to derive surrogate models mimicking the behavior of the physics-based simulators with smaller computational cost, and Bayesian Optimization is used to predict which manufacturing parameters need to be adopt in order to improve the quality of the electrochemically active interfaces. In my lecture I provide, in comparison with experimental data, application examples of our approach to several formulations and chemistries representative for lithium ion and solid state battery cell applications, for both solvent-based and dry processing approaches. Finally, I discuss our latest developments of Virtual and Mixed Reality tools to assess the virtually produced electrode microstructures and interfaces, and to train students and factory operators about the electrode manufacturing-microstructure-performance links.