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.
Temperature is an important factor for an optimal battery performance. To gain knowledge about the internal temperature distribution in a battery, many thermal simulation studies are performed. Among other factors, they differ in the level of homogenization (LoH) of the geometry, which directly influences the computing time. However, the effects of different LoH, in particular of the cell layers, on the modeling and prediction quality of the temperature field are scarcely investigated. This work discusses the effect of different LoH of the cell stack on a numerical 3D thermal battery model for different thermal management strategies. A new approach of reducing the number of cell layers of the pouch cell geometry while keeping their volumetric proportions constant is proposed. It is clearly shown that the LoH has a large impact on the thermal transport paths, especially through the current collectors and tabs, and therefore on the predicted internal temperature distribution. In addition, the effect of the LoH differs for different thermal management strategies, because they affect the heat transport paths as well.
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