The efficient and reliable modeling of battery cells is a key enabler for improving performance, lifetime and safety in various industrial applications, especially under the increasing demand for advanced energy storage solutions. This mini-symposium addresses micro-macro simulation approaches, focusing on the individual constitutive modeling of anodes, cathodes, and separators, along with the challenges of homogenization to model the mechanical, electrical, and thermal behavior of battery cells and whole battery systems. Therefore, all possible aspects of multi-physical modeling, that could typically include the solution and coupling of mechanical, electrical, chemical, thermal and fluid fields, are targeted. Coated anodes, typically composed of graphite or silicon, exhibit distinct material properties that significantly influence battery performance. Graphite anodes, while stable and well-understood, are limited by their capacity and lithium-ion diffusion rates, which can lead to capacity fade. On the cathode side, materials such as lithium nickel manganese cobalt oxide (NMC) and lithium iron phosphate (LFP) present varying performance profiles. Separators, which serve as barriers to prevent short circuits while allowing ionic transport, must also be carefully considered. Commonly used materials like polyethylene (PE) and polypropylene (PP) must balance mechanical strength and ionic conductivity. The separator’s properties can vary based on the design of the cell—whether it is a prismatic stack or a cylindrical winding—impacting the overall performance and safety of the battery. A significant challenge in the industrial application of these technologies is the homogenization of the complex behaviors exhibited by these materials, since the size of high-fidelity models is prohibitive in many industrial applications. Various interaction effects due to charging, cooling, swelling and even mechanical impact on battery systems are of utmost importance for the industrial application of battery systems. This mini-symposium should highlight cutting-edge coupled simulation methods, including AI-driven approaches, that facilitate the integration of detailed microstructural data into macroscopic models. By leveraging machine learning techniques, it is expected to enhance the predictive accuracy of models, enabling real-time optimization of battery designs and performance under various operational conditions.