Authors: Philipp Seegert | Sabrina Herberger | Anne Heß | Raphael Mühlpfort | Leonie Pfeifer | Thomas Wetzel
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

(<i>Invited</i>) Simulative and Experimental Investigation of Thermal Battery Behavior – from Microstructure to Cell Level The performance as well as the lifetime of lithium-ion batteries, which are defined by the coupled multiphysical transport and reaction processes within the cells are strongly temperature-dependent. Efficient thermal management systems are therefore needed in the automotive application to achieve optimal performance, increase lifetime and provide safety against thermal runaway. These systems keep the battery cells within an optimal specific temperature window through external cooling and possible heating. However, it is not only the temperature level that plays a decisive role, but also the influence of time-varying inhomogeneous temperature distributions, which can arise or be amplified by the heat release within the cell or the interaction with the external thermal management concepts. Local performance limitations as well as local degradation effects can be caused and intensified within the cells by an inhomogeneous thermal behavior, which is particularly pronounced for large-format automotive cells. Therefore, a thorough understanding of the thermal cell behavior is required, which is defined by the interaction of a variety of effects. These effects range from the microstructure level of the electrodes to the cell design and the interplay with the external thermal management concept. The crucial heat transfer paths derive from the combination of the internal cell design and the external cooling application. The thermophysical properties of battery cells result from the material configuration of the components at the microstructure level. In addition, the heat release within the battery cell during operation is defined by a combination of effects at the microstructure and cell level. This contribution focuses on a simulative and experimental investigation of the thermal battery behavior, from the microstructure to the cell level, and its interplay with the performance and aging of lithium-ion battery cells. At the microstructure level, the experimental determination of the temperature-dependent effective thermal properties of battery components is presented using a characterization methodology in a bottom-up approach. In addition, three-dimensional simulation models that resolve the heterogeneous microstructure of battery electrodes were developed using a stochastic drop-and-roll approach and efficient surrogate models were derived. These simulation models enable the investigation of the effective thermal transport based on the desired material composition and microstructure parameters (such as porosity as well as particle shape, size, and contact) and thereby the acceleration of the determination and prediction of thermophysical properties. The experimental characterization provides the necessary parameterization and validation for the thermal simulation models. In order to investigate the impact of degradation effects on the thermal transport, pouch cells from cyclic aging studies with different homogenous and inhomogeneous temperature distributions during aging were investigated and the thermophysical changes on the individual battery electrodes of these cells were analyzed on the microstructure level. At the cell level, detailed three-dimensional simulation models were used to investigate the interaction of internal cell behavior and various external thermal management concepts. The insights gained into the thermal behavior from the microstructure to the cell level were used to develop thermal substitute cells as an experimental development tool for the validation of thermal management systems. These substitute cells replicate the external geometry and thermal behavior of real lithium-ion battery cells, but do not contain any electrochemical components. As a result, the costs and safety effort for validation at module or pack level can be significantly reduced compared to real battery cells Microstructure ; Battery (electricity) ; Materials science ; Thermal ; Engineering physics ; Composite material ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science