Modeling the Thermal Conductivity of Porous Electrodes of Li‐Ion Batteries as a Function of Microstructure Parameters

Abstract

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.

Investigation of Drying Curves of Lithium‐Ion Battery Electrodes with a New Gravimetrical Double‐Side Batch Dryer Concept Including Setup Characterization and Model Simulations

“…The considered length s for both mass and heat transfer is the dry film thickness of the electrode with a value of 83 μm (see Table 3 ). To account for the worst case, which is a completely dry electrode compared to a semiwet or wet electrode, the thermal conductivity of the dry electrode was considered with λ film = 2.46 W m −1 K −1 based on experimentally determined values found by Oehler et al [ 17 ] For the heat transfer coefficient α top , a value of α top = 39.3 W m −2 K −1 (see Table 4 ) is assumed, as explained in more detail in the following. The mass transfer coefficient is derived from the value of the heat transfer coefficient (see Section 3.4.1) with .…”

Investigation of the Effective Thermal Conductivity of Cell Stacks of Li‐Ion Batteries

“…In our previous publication, [ 37 ] we presented a generic geometry generation routine for porous electrode coatings. In this work, the structure generation routine developed is extended by current collectors and separator layers so that the effective thermal conductivity of electrode stacks and cell stacks can be determined.…”

“…Reproduced with permission. [ 37 ] Copyright 2020, Wiley.…”

“…Therefore, the concept of the effective thermal conductivity is used. As reported in our previous publication, [ 37 ] the effective thermal conductivity of porous electrode coatings or layered composites can be calculated by applying a temperature gradient. The latter induces a heat flow which, according to the second law of thermodynamics, flows from a higher to a lower temperature.…”

“…In this regard, we present in this work a new modeling concept for the determination of the effective thermal conductivity of porous anode stacks, cathode stacks and separators, and homogeneous cell stacks. Therefore, our analytical and numerical model approaches [ 37 ] for porous electrode coatings presented previously, which consider a large set of microstructural parameters and the thermal bulk material properties, have been extended to electrode and cell stacks. The analytical model is a precise and fast model, representing the various complex thermal transport paths within the porous electrode coatings.…”