Computational Microstructure Exploration for Batteries: Leveraging High Performance Computing

Abstract

Battery performance is strongly influenced by physical and electrochemical processes occurring on the pore scale. Thus, especially the microstructure of battery components is important. Therefore, battery research is highly interested in understanding the interdependence of both. Our work goes into this direction applying computational methods that are used in the field of battery research only recently. Those are the lattice Boltzmann method (LBM) and pore network modeling (PNM). Both are further developed and used in a smart and complementary manner. The application of which is presented for two recent research topics: 1) the influence of chemical surface reactions on transient battery morphologies, and 2) the identification of representative elementary volumes for reducing computational cost. Both topics are placed in the context of our efforts, i.e., applying high performance computing tools for developing and understanding battery technologies.