Stochastic 3D modeling of the microstructure of lithium-ion battery anodes via Gaussian random fields on the sphere

Feinauer, Julian; Brereton, Tim; Spettl, Aaron; Weber, Michael; Manke, Ingo; Schmidt, Volker

doi:10.1016/j.commatsci.2015.06.025

Cited by 54 publications

(73 citation statements)

References 53 publications

(67 reference statements)

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“…In [16] a stochastic 3D model for the anode microstructure of so-called energy cells in Li-ion batteries has been developed on the basis of experimental image data obtained by X-ray tomography, see Figure 1. The anode mainly consists of a connected network of graphite particles and pores between the particles.…”

Section: Lithium-ion Batteriesmentioning

confidence: 99%

“…The anode material is completely described by the barycenters of particles and their spherical harmonics coefficients. In [16] statistical analysis of this parametric representation is the basis for stochastic 3D modeling of anode microstructures in Li-ion batteries. Connections between particles have been modeled by a random graph (2).…”

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

“…Besides a visual comparison between virtual microstructures generated by the fitted model and experimental image data, see Figure 4, an extensive quantitative validation regarding certain microstructure characteristics has been performed and discussed in [16]. Exemplarily, the spherical contact distribution functions [10] of the particle phase, which are nearly identical for virtual and experimental microstructures, are shown in Figure 4.…”

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

“…Spherical contact distribution functions computed for experimental (red) and simulated (blue) microstructures are nearly identical (right). 5 Thus, in [15] and [16] flexible modeling techniques have been developed for 3D microstructures in Li-ion batteries. In particular, a stochastic 3D model has been established which is able to generate virtual microstructures that are statistically similar to experimental microstructures.…”

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

“…The paper is organized as follows. Models for electrode materials in Li-ion batteries ( [15], [16]), and for SOFC ( [13], [17]) are reviewed in Sections 2 and 3, respectively. In Section 4 we present a method to derive empirical formulas that describe the quantitative relationship between microstructure characteristics and effective conductivity in porous microstructures ( [11], [12]).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

Neumann¹,

Schmidt²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. We are going to introduce the concept of stochastic 3D modeling of geometrically complex (disordered) microstructures as a tool for virtual materials testing, including applications to battery electrodes as well as to electrodes of fuel cells, and solar cells. Using stochastic 3D models, one can generate a large variety of stochastically simulated micrsotructures with little computational effort. These virtual microstructures can be used as data basis to elucidate microstructure-property relationships. In this way, for example, effective conductivity can be expressed by microstructural characteristics such as volume fraction, tortuosity (windedness of transport paths) and costrictivity (bottleneck criterion) of the considered material phase. In another recent simulation study, we analysed more than 8000 virtual microstructures for various microstructural scenarios. Using data mining techniques like artificial neural networks and random forests, we were able to accurately predict effective conductivities given microstructure properties like volume fraction, tortuosity and constrictivity.

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Section: Lithium-ion Batteriesmentioning

confidence: 99%

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

Section: Stochastic Modeling Of Anode Microstructurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

Neumann¹,

Schmidt²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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The Effects of Constriction Factor and Geometric Tortuosity on Li‐Ion Transport in Porous Solid‐State Li‐Ion Electrolytes

Hamann

Zhang

Gong

et al. 2020

Adv Funct Materials

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3D focused ion beam tomography is used to analyze the microstructures of Li‐ion conducting Li6.75La2.75Ca0.25Zr1.5Nb0.5O12 (LLCZN) garnet porous electrolytes with different levels of porosity and the theoretical effective bulk conductivities of the electrolyte are calculated based on LLCZN volume fraction, constriction factor, geometric tortuosity, and percolation factor. The experimentally measured effective bulk conductivities are consistently lower than the theoretical values when assuming constant bulk conductivity, suggesting the bulk conductivity of the LLCZN decreased with increasing porosity. This work highlights the importance of understanding the full effects of altering the microstructure of solid‐state electrolytes, as this will play a key role in advancing Li‐ion battery technology to higher energy and power densities.

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Understanding Electrolyte Filling of Lithium‐Ion Battery Electrodes on the Pore Scale Using the Lattice Boltzmann Method

Lautenschlaeger

Prifling

Benjamin

et al. 2022

Batteries & Supercaps

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Electrolyte filling is a time-critical step during battery manufacturing that also affects battery performance. The underlying physical phenomena mainly occur on the pore scale and are hard to study experimentally. Therefore, here, the lattice Boltzmann method is used to study the filling of realistic 3D lithium-ion battery cathodes. Electrolyte flow through the nanoporous binder is modelled adequately. Besides process time, the influences of particle size, binder distribution, volume fraction and wetting behavior of active material and binder are investigated. Optimized filling conditions are discussed by pressure-saturation relationships. It is shown how the influencing factors affect the electrolyte saturation. The amount and distribution of entrapped residual gas are analyzed in detail. Both can adversely affect the battery performance. The results indicate how the filling process, the final electrolyte saturation, and also the battery performance can be optimized by adapting process parameters as well as electrode and electrolyte design.

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Stochastic 3D modeling of the microstructure of lithium-ion battery anodes via Gaussian random fields on the sphere

Cited by 54 publications

References 53 publications

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

Stochastic 3d Modeling of Amorphous Microstructures - A Powerful Tool for Virtual Materials Testing

The Effects of Constriction Factor and Geometric Tortuosity on Li‐Ion Transport in Porous Solid‐State Li‐Ion Electrolytes

Understanding Electrolyte Filling of Lithium‐Ion Battery Electrodes on the Pore Scale Using the Lattice Boltzmann Method

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