Modelling of lithium-ion battery electrode calendering: A critical review

Abstract

Lithium-ion Batteries (LIBs) are central to modern energy storage, with growing demands for improved performance, safety, and cost efficiency. Electrode calendering, a critical step in LIBs manufacturing, significantly influences the microstructure and electrochemical properties of electrodes. This review explores advances in the modelling of the calendering process over the past few years, focusing on empirical, numerical, and machine learning approaches. Empirical models, though computationally efficient, are limited by oversimplification, while numerical methods, such as Discrete Element Method (DEM) and Finite Element Method (FEM), offer more detailed insights into the structural evolution during calendering but require intensive computational resources. The growing application of machine learning introduces novel data-driven methods for optimising the process by effectively handling multiscale phenomena and high-dimensional data. A comparative analysis of these modelling strategies highlights the need for hybrid approaches that integrate empirical, numerical, and data-driven models to accurately predict electrode behaviour and optimise calendering conditions. Future research should aim to bridge the gap between computational accuracy and practical application to improve the performance and cost-efficiency of LIBs manufacturing.