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The performance and lifetime of lithium‐ion batteries are strongly influenced by the temperature distribution within the cells, as electrochemical reactions, transport properties, and aging effects are temperature dependent. However, thermal analysis and numerical simulation of the temperature inside the cells can only be as accurate as the underlying data on thermal transport properties. This contribution presents a numerical and analytical model for predicting the thermal conductivity of porous electrodes as a function of microstructure parameters. Both models account for the morphology of the electrode structures and bulk material properties of the constitutive components. Structural parameters considered in both models alike are the porosity of the electrode coatings, particle size distribution, particle shape, particle contact areas, and binder carbon black distribution. The numerical model is based on the well‐established finite volume discretization, allowing for detailed 3D analyses. The analytical model is an extension of the well‐known Zehner–Bauer–Schluender approach for solid packing and provides fast predictions of the effects of parameter variations. The results of both models have been successfully verified against each other and compared to literature data and experimental measurements.

Knowledge of the thermal transport properties of the individual battery components and their combination is required for the design of thermally optimized lithium‐ion batteries. Based on this, the limiting components can be identified and potentially improved. In this contribution, the microstructures of commercial porous electrode coatings, electrode stacks, and cell stacks are reconstructed based on experimentally determined structure parameters using a specifically developed structure generation routine. The effective thermal conductivity of the generated stacked structures is then determined by a numerical tool developed in‐house based on the finite‐volume method. The results are compared with an analytical model for fast accurate predictions which takes the morphological parameter sets and the geometry of the stacks into account. Both models are used to identify the system‐limiting components via selected simulation studies. Finally, the results of both models are compared with experimental data for commercial electrode stacks and common literature values for cell stacks.

The impact of electrode formation is studied by the spatially and time‐resolved distribution of transverse relaxation. In situ 7Li nuclear magnetic resonance experiments are performed on an experimental lithium‐ion battery cell to study the impact of electrode passivation via imaging and transverse relaxation in the interelectrode volume. The electrolyte in the battery, using technically relevant electrode material, i.e., graphite and lithium–nickel–cobalt–manganese–oxide, is studied by 2D magnetic resonance imaging. The electrolyte is 1 mol L−1 lithium hexafluorophosphate dissolved in a binary mixture of ethylene carbonate and dimethyl carbonate. 1D profiles are acquired and related to 7Li concentration during passivation and during a constant current/constant voltage cycle. The transverse relaxation rate R2(z,t) measured by multiecho profiles revealed changes within the electrolyte volume. The ongoing process changes the relaxation distribution. Indications for a defective electrode passivation are deduced from the data. During one charging cycle with constant current/constant voltage, the lithium concentration is measured spatially resolved, and the data are modeled by the Nernst–Planck equation.

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