(577f) Electrochemical-Mechanical Microstructure-Scale Battery Modeling for Improved Performance and Degradation Predictions during Fast Charging. | AIChE

(577f) Electrochemical-Mechanical Microstructure-Scale Battery Modeling for Improved Performance and Degradation Predictions during Fast Charging.


Usseglio Viretta, F. - Presenter, National Renewable Energy Laboratory
Sitaraman, H., National Renewable Energy Laboratory
Brazell, M., National Renewable Energy Laboratory
Allen, J., National Renewable Energy Laboratory
Smith, K., NREL
Day, M., National Renewable Energy Laboratory
Battery performance is strongly correlated with electrode microstructure. Macro-homogeneous electrochemical models typically use porous electrode theory and thus abstract microstructural heterogeneity of composite battery electrodes using effective macroscopic properties [1]. Such models only require a small number of degrees of freedom, however they are intrinsically limited by their macroscale approach and related assumptions. In particular, in-plane heterogeneity is neglected, and particles are assumed spherical which, respectively, hinder degradation predictions and relevance for materials with more complex particle morphologies such as graphite.

Microstructure-scale models remedy these limitations by directly solving the governing equations on the electrode’s constituent geometry [2]. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the system at the microstructure scale and be representative. In this work [3], a microstructure-scale coupled electrochemical-mechanical model is developed. The solver uses a level-set method for modeling electrode-electrolyte interfaces and surface reactions. It is built on a block-structured adaptive-mesh-refinement framework using open-source library, AMReX [4], and can currently utilize emerging heterogenous (CPU+GPU) computing architectures. This approach simplifies mesh generation for complex microstructure geometries and leverages fast and scalable multi-grid based linear solvers on Cartesian grids. Simulations of fast charging for automotive application, with electrode geometries obtained through Computed-Tomography imaging are presented (cf. figure below). Concentration gradients, in-plane heterogeneities, cell-voltage and potential for lithium plating are discussed and compared with a baseline macroscale model. In addition, stress distribution is analyzed within the electrode volume. The model demonstrates excellent mass conversation and high numerical scaling efficiency on high-performance computers (HPC), both for CPU and GPU architectures.

[1] F. Usseglio-Viretta et. al., Journal of The Electrochemical Society,165(14) A3403-A3426 (2018), https://doi.org/10.1149/2.0731814jes

[2] J. Allen et al., Journal of Scientific Computing, 86:42 (2021), https://doi.org/10.1007/s10915-021-01410-5

[3] Article in preparation.

[4] https://amrex-codes.github.io/