Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning

Both society and market calls for safer, high-performing and cheap Li-ion batteries (LIBs) in order to speed up the transition from oil-based to electric-based economy. One critical aspect to be taken into account in this modern challenge is LIBs manufacturing process, whose optimization is time and resources consuming due to the several interdependent physicochemical mechanisms involved. In order to tackle rapidly this challenge, digital tools able to optimize LIBs manufacturing parameters are crucially needed for both well-known and recently discovered chemistries. The methodology presented here encompasses experimental characterizations, in silico generation of electrode mesostructures and machine learning algorithms to track the effect of the calendering process over a wide array of mesoscale electrode properties critically linked to the electrochemical performance. Particularly, features as the interconnectivity of the particles network, the electrolyte tortuosity and effective ionic conductivity, the percentage of current collector surface covered by either active material or carbon-binder domain particles and the active material surface in contact with electrolyte were analysed and discussed in detail. This approach was tested and validated for the case of LiNi1/3Mn1/3Co1/3O2-based cathodes calendering, proving its capability to ease the process parameters-electrode properties interdependencies analysis, paving the way to deeper understanding of LIBs manufacturing. © 2020 The Authors