A Particle-Based Model Reduction for Lithium-Ion Batteries

The performance of lithium-ion battery cells is primarily determined by their electrode material and electrode structure. For the optimization of electronic devices, it is crucial to develop computational models that accurately predict the interaction between the electrode structure and the battery operation while maintaining low model complexity. The state-of-the-art optimization model, the Doyle-Fuller-Newman model, approximates the entire active material as effective spheres of uniform size. Here, we propose a particle-based reduction of the solid-state diffusion process that allows to take different particle sizes and shapes directly into account. This particle-based model reduction is based on the evolution of a structural property which captures the influence of the process parameters and of the geometry on the particle state, such as the surface concentration of lithium. Within the evolution approach a so-called shape-dependent function is used to quantify the influence of the particle’s geometry. Three numerical experiments are used to verify the particle-based model reduction for electrode geometries with different non-spherical microstructures at low and high charging rates. We show that the proposed model predicts the particle states and thus the battery performance equally well or better than the Doyle-Fuller-Newman model with comparable computational effort.