Most cathode materials for lithium-ion batteries exhibit a low electronic conductivity. Hence, a significant amount of conductive graphitic additives are introduced during electrode production. The mechanical stability and electronic connection of the electrode is enhanced by a mixed phase formed by the carbon and binder materials. However, this mixed phase, the carbon binder domain (CBD), hinders the transport of lithium ions through the electrolyte pore network. Thus, reducing the performance at higher currents. In this work we combine microstructure resolved simulations with impedance measurements on symmetrical cells to identify the influence of the CBD distribution. Microstructures of NMC622 electrodes are obtained through synchrotron X-ray tomography. Resolving the CBD using tomography techniques is challenging. Therefore, three different CBD distributions are incorporated via a structure generator. We present results of microstructure resolved impedance spectroscopy and lithiation simulations, which reproduce the experimental results of impedance spectroscopy and galvanostatic lithiation measurements, thus, providing a link between the spatial CBD distribution, electrode impedance, and half-cell performance. The results demonstrate the significance of the CBD distribution and enable predictive simulations for battery design. The accumulation of CBD at contact points between particles is identified as the most likely configuration in the electrodes under consideration.

The effect of the mixing and drying process on the microstructure of ultra‐thick NCM 622 cathodes (50 mg cm−2, 8 mAh cm−2) and its implication for battery performance is investigated. It is observed that the shear force during the mixing process significantly influences the resulting microstructure with regard to binder migration during the drying process. Based on the information extracted from scanning electron microscopy–energy dispersive X‐ray spectroscopy (SEM–EDX) cross sections, the carbon binder domain (CBD) is distributed in the pore space of virtual electrodes generated by a stochastic 3D microstructure model. Simulations predict a CBD configuration that leads to optimal performance of the electrode. Furthermore, it is shown that a low drying rate has a beneficial influence toward the rate capability of the ultra‐thick cathodes. The specific energy of an ultra‐thick cathode is 18% higher compared with a cathode prepared according to the state of the art. With an improved process in a pilot scale, the advantage can be kept up to current densities of at least 3 mA cm−².

In traditional Li‐ion batteries, the electrolyte consists of a Li‐conducting salt dissolved in organic solvents at a concentration of ∼ 1 mol L−1 (1 M). In this work, we use increased LiPF6 concentrations between 1 and 2.3 M to investigate the influence of the electrolyte salt concentration on the rate capability of ultra‐thick (49.5 mg cm−2) and thin (5.6 mg cm−2) NCM 622 electrodes, respectively. At higher electrolyte salt concentrations than 1 M, thin electrodes suffer from increased polarization, due to a higher viscosity and a reduced ionic conductivity. In contrast, by raising the salt concentration from 1 to 1.9 M the discharge capacity of ultra‐thick electrodes is increased by more than 50 % for current densities above 3 mA cm−2, which significantly improves their rate capability. 3D microstructure resolved simulations revealed that this effect results from the mitigation of Li‐ion depletion in the electrolyte filled pore space of ultra‐thick electrodes.

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access.

It is well known that the microstructure of electrodes in lithium-ion batteries has an immense impact on their overall performance. The manufacturing of the batteries includes the so-called calendering, where the electrodes are compressed with a certain pressure, which is called compaction load. This process step mainly determines the resulting morphology of the electrode and thus the properties of the battery. Therefore, eight cathodes with different compaction loads were manufactured and imaged by synchrotron tomography, which leads to 3D images containing detailed information about the inner structure of the cathode. This image data allows an extensive analysis of the 3D cathode microstructure with respect to increasing compaction. In order to quantify the microstructural changes we use several characteristics describing diverse properties of the morphology. Furthermore, the 3D image data can be used for the computation of characteristics which can not be determined by experiments. Therefore, 3D image data allows us to understand how the microstructure of cathodes is influenced by the compaction load. Finally, we are able to predict the distribution of a certain characteristic for arbitrary compaction loads. This information is valuable with regard to the development of improved lithium-ion batteries.

Lithium-ion batteries are the dominating electrochemical energy storage technology for battery electric vehicles. However, additional optimization is needed to meet the requirements of the automotive industry regarding energy density, cost, safety, and fast charging performance. In conventional electrode designs, there is a trade-off between energy density and rate capability. Recently, three-dimensional (3D) structuring techniques, such as laser perforation, were proposed to optimize both properties at the same time and remarkable improvements in fast-charging performance have been demonstrated. In this work, we investigate the effect of structuring techniques on the thermal properties and electrochemical performance of the battery using microstructure-resolved simulations. Particular attention will be paid to the heat evolution and lithium plating during fast charging of the batteries. According to our results, 3D structuring is able to reduce the overall cell resistance by improving the electrolyte transport. This has a positive impact on the fast charging capability of the cell and, moreover, reduces the danger of lithium plating.

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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