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- (221b) Multi-Scale Modelling of Battery Electrode Calendering Using a Combined DEM-FEM Approach
2025 AIChE Annual Meeting
(221b) Multi-Scale Modelling of Battery Electrode Calendering Using a Combined DEM-FEM Approach
The rapid and cost-effective design of efficient and reliable manufacturing processes is key to maximizing profitability in battery manufacturing operations and enabling the green energy transition. Electrode calendering is a critical step of the battery manufacturing process that affects important battery performance attributes such as capacity, discharge rate and longevity, but has presented challenges with respect to process design [1,2].
The primary goal of calendering is to produce an electrode with specific micro-structural characteristics (tortuosity and micro-porosity) via roller compaction. The electrode is a mechanically complex particle composite and the effects of calendaring process parameters (layer thickness, roll gap, roll speed) and particle properties (particle size distribution, morphology, strength) on its micro-structure are not fully understood. This results in an over-reliance on time-consuming and expensive physical trials in the process design stage. Numerical modelling can be a source of much needed insight in this context.
Particle-based methods such the Discrete Element Method (DEM) are uniquely capable of informing the relationship between particle properties and the electrode micro-structure [1,2] but are too computationally expensive for process scale modelling. Continuum methods such as the Finite Element Method (FEM) have the required computational efficiency but are incapable of resolving the electrode micro-structure. A multi-scale modelling methodology that combines the two methods is therefore required to enable practical solutions at the process scale.
This talk presents a computationally efficient multi-scale modelling methodology for battery electrode calendering that combines micro-scale DEM simulations of a Representative Volume Element (RVE) of the electrode with explicitly integrated process scale FEM simulations. The validity, advantages and limitations of the methodology are evaluated and discussed.
References
[1] D. Schreiner, A. Klinger, G. Reinhart, Modeling of the calendering process for lithium-ion batteries with DEM simulation, Procedia CIRP 93 (2020) 149–155.
[2] R. Ge, D.J. Cumming, R.M. Smith, Discrete element method (DEM) analysis of lithium ion battery electrode structures from X-ray tomography-the effect of calendering conditions, Powder Technol 403 (2022) 117366.
The primary goal of calendering is to produce an electrode with specific micro-structural characteristics (tortuosity and micro-porosity) via roller compaction. The electrode is a mechanically complex particle composite and the effects of calendaring process parameters (layer thickness, roll gap, roll speed) and particle properties (particle size distribution, morphology, strength) on its micro-structure are not fully understood. This results in an over-reliance on time-consuming and expensive physical trials in the process design stage. Numerical modelling can be a source of much needed insight in this context.
Particle-based methods such the Discrete Element Method (DEM) are uniquely capable of informing the relationship between particle properties and the electrode micro-structure [1,2] but are too computationally expensive for process scale modelling. Continuum methods such as the Finite Element Method (FEM) have the required computational efficiency but are incapable of resolving the electrode micro-structure. A multi-scale modelling methodology that combines the two methods is therefore required to enable practical solutions at the process scale.
This talk presents a computationally efficient multi-scale modelling methodology for battery electrode calendering that combines micro-scale DEM simulations of a Representative Volume Element (RVE) of the electrode with explicitly integrated process scale FEM simulations. The validity, advantages and limitations of the methodology are evaluated and discussed.
References
[1] D. Schreiner, A. Klinger, G. Reinhart, Modeling of the calendering process for lithium-ion batteries with DEM simulation, Procedia CIRP 93 (2020) 149–155.
[2] R. Ge, D.J. Cumming, R.M. Smith, Discrete element method (DEM) analysis of lithium ion battery electrode structures from X-ray tomography-the effect of calendering conditions, Powder Technol 403 (2022) 117366.