To effectively control the coating quality, the operational conditions at the macroscale, such as airflow velocity, temperature, and drying rate, must be precisely managed. However, the ultimate coating characteristics are determined by phenomena at the microscale, including internal solvent distribution and thickness, and at the mesoscale, such as particle interactions and microstructure formation. Thus, a comprehensive multiscale modeling approach that captures interactions across all scales is required. This study proposes a multiscale model to quantitatively analyze the interactions between macro-scale drying conditions and micro/meso-scale coating properties, aiming to optimize the conditions for high-quality separator coatings.
Our multiscale framework integrates three distinct scales. At the macroscale, computational fluid dynamics (CFD) simulated solvent evaporation and airflow dynamics within a floating-type oven [2] featuring impinging jets from slot nozzles. At the microscale, differential algebraic equation (DAE) modeling predicts the solvent concentration, temperature distribution, and coating thickness by utilizing heat transfer coefficients, partial pressures, and zone temperatures obtained from CFD [3,4,5]. At the meso-scale, a coarse-grained molecular dynamics (CGMD) model [6] was used to analyze the slurry particle interactions and microstructure. A macro-micro-meso data exchange platform was also prepared to facilitate integration across these scales and is currently under development. An iterative two-way coupling between the CFD and DAE models ensures a continuous exchange of boundary condition data, such as the evaporative mass flux and surface temperature, at each time step.
Multiscale modeling successfully provided quantitative predictions of the localized temperature, solvent distribution, thickness evolution, and drying rates during simultaneous two-sided drying, which was unattainable using conventional single-scale approaches. Notably, the distinct drying characteristics of the upper and lower surfaces clarified the root cause of the coating coverage nonuniformities. These results are essential for identifying optimal drying conditions, minimizing front-to-back quality variations, and enabling the production of uniformly coated separators. Ultimately, the proposed method contributes significantly to enhanced coating quality, manufacturing efficiency, and reduced-energy consumption.
Future research will integrate the meso-scale CGMD model into the established macro-micro-meso data exchange platform to complete the multiscale framework. Additionally, uncertainty quantification will be used to assess realistic operational variabilities, such as variations in temperature, humidity, and equipment fluctuations, and surrogate-based Model Predictive Control (MPC) [7] will be implemented for real-time process optimization and quality enhancement.
Reference
[1] Costa, C. M., Lee, Y. H., Kim, J. H., Lee, S. Y., & Lanceros-Méndez, S. (2019). Recent advances on separator membranes for lithium-ion battery applications: From porous membranes to solid electrolytes. Energy Storage Materials, 22, 346-375.
[2] PNT Inc., Apparatus for coating both sides of thin material, Korea Patent 1020120041143, 2012.
[3] A. Mesbah, A. N. Ford Versypt, X. Zhu, and R. D. Braatz, Nonlinear model-Based control of thin-film drying for continuous pharmaceutical manufacturing. Industrial & Engineering Chemistry Research, 2014. 53(18):7447-7460.
[4] Dias, B. S., et al. (2022). "Multi-scale modeling and simulation of IR radiative drying for coil coating processes." Drying Technology 40(16): 3466-3482.
[5] Susarla, N., et al. (2018). "Modeling and analysis of solvent removal during Li-ion battery electrode drying." Journal of Power Sources 378: 660-670.
[6] Yao, X., et al. (2023). "Coarse-grained molecular dynamics simulations of microstructure evolution and debonding in water-based cathode electrode drying." Journal of Materials Processing Technology 321.
[7] J. A. Paulson, A. Mesbah, S. Streif, R. Findeisen, Fast stochastic model predictive control of high-dimensional systems. in Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 2802-2809, 2014.