Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes

2024-09-17

AI Summary

Accurate, realistic, and controllable 3D modeling of the nanoscale porous microstructure formed by binder–conductive additive phases in lithium-ion battery cathodes remains challenging. Method: This study proposes a data-driven stochastic 3D microstructure modeling framework that synergistically integrates Boolean models (ellipsoidal/spherical particles) with Gaussian random field excursion sets—enabling concurrent multi-phase nanoscale pore characterization for the first time. Model fidelity and parametric tunability are rigorously validated via FIB-SEM 3D image analysis and multi-metric morphological quantification. Contribution/Results: The resulting model faithfully reproduces key microstructural features of real electrodes and quantitatively uncovers a nonlinear dependence of effective ionic/electronic conductivity in the binder–conductive additive phase on graphite content. This establishes a scalable virtual experimentation paradigm for electrode material design targeting transport-property optimization.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Matrix & Tensor Methods
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. The model parameters are calibrated to 3D image data of cathodes in lithium-ion batteries acquired by focused ion beam scanning electron microscopy. Subsequently, model validation is performed by comparing model realizations with measured image data in terms of various morphological descriptors that are not used for model fitting. Finally, we use the stochastic 3D model for predictive simulations, where we generate virtual, yet realistic, image data of nanoporous binder-conductive additives with varying amounts of graphite particles. Based on these virtual nanostructures, we can investigate structure-property relationships. In particular, we quantitatively study the influence of graphite particles on effective transport properties in the nanoporous binder-conductive additive phase, which have a crucial impact on electrochemical processes in the cathode and thus on the performance of battery cells.

Problem

• Modeling nanoporous binder-conductive additive phase in battery cathodes
• Calibrating model parameters using 3D image data
• Investigating graphite particles' impact on transport properties

Innovation

• Boolean model with ellipsoidal grains for graphite
• Gaussian random field for carbon black-binder mix
• Boolean model with spherical grains for large pores