Simulation of Structure Formation during Drying of Lithium‐Ion Battery Electrodes using Discrete Element Method

Abstract

Lithium‐ion batteries are state‐of‐the‐art and still their performance is subject to constant improvement. These enhancements are based, among other things, on optimization in the electrode production process chain. High optimization potential exists for the drying process of electrodes, as aiming for high drying speeds can greatly reduce both, investment costs and operating costs of the drying. However, high drying rates without appropriate precautions go hand in hand with poorer cell performance and adhesive strength, leading to a conflict between the required performance and production costs of the electrodes. Herein, a numerical approach based on the discrete element method to describe the formation of the electrode structure during drying is presented. The focus is placed on the active material structure and the effects due to particle interactions. Herein, a direct numerical description of the fluid phase is avoided by using various fluid substitute models, so that the simulation time and the computational costs can be greatly reduced. The model is validated by simulating different electrode areal loadings and comparing the achieved layer thicknesses to experimental results of the electrode drying process. A high agreement between experiment and simulation regarding density is obtained for different areal loadings.

A Computational Fluid Dynamics–Discrete Element Method Model for Physics‐Based Simulation of Structure Formation during Battery Electrode Drying

“…Srivastava et al and Lippke et al used a discrete element method (DEM) approach, where active material and CBD particles were regarded as granular spheres interacting via Hertzian contact forces and cohesive forces. [47,48] The fluid in both approaches was considered as an implicit background fluid acting on the particles via Stokesian viscous drag. In case of Srivastava et al, the drying process was simulated by compressing the simulation box from the top until a desired volume fraction was reached.…”

Insights into Influencing Electrode Calendering on the Battery Performance

“…Forouzan et al [56] used shifted-force Lennard-Jones potential functions and granular Hertzian force to simulate particle interactions and evaluate the particle arrangement resulting from processing parameters. Lippke et al [57] modeled the structure formation during Figure 5. a) Demonstrates a computational workflow, starting with the slurry (coarsed grain molecular dynamics, CGMD, simulation), to the dried electrode (CGMD simulation), to the calendered electrode (discrete element method, DEM, simulation) and finally the assessment of the electrochemical performance (with continuum finite element method, FEM, models).…”

“…Forouzan et al [ 56 ] used shifted‐force Lennard–Jones potential functions and granular Hertzian force to simulate particle interactions and evaluate the particle arrangement resulting from processing parameters. Lippke et al [ 57 ] modeled the structure formation during drying using the discrete element method (DEM) by using surrogate models for relevant fluid effects and could predict the coating porosity for different mass loadings. As most electrode microstructural simulations focus on spherical particles, Becker et al [ 58 ] Modeled the influence of particle shape on the mechanical compression and resulting transport properties of electrodes for non‐spherical particles, using a novel random close packing algorithm.…”

Numerical Investigation of Conductivity Additive Dispersion in High‐Power and High‐Energy NMC‐Based Lithium‐Ion Battery Cathodes: Application‐Based Guidelines

“…In chronological order of cell manufacturing, dry as well as wet mixing was investigated by Bockholt et al [ 6,7 ] and Bauer et al, [ 8 ] followed by studies on dispersion by Mayer et al [ 9 ] and Dreger et al [ 10,11 ] . Subsequently, the influence of drying was reported by Lombardo et al [ 12 ] and Lippke et al [ 13 ] . Finally, Ngandjong et al [ 14 ] and Primo et al [ 15 ] delivered insights into calendering in their studies.…”