Batteries are essential in numerous applications, such as mobile devices or electric vehicles, and are a key enabler for the transition towards renewable energy. Recent developments of novel cell technologies, such as solid-state batteries or sodium-ion batteries, promise significant improvements in energy density, safety, or sustainability. These are key criteria for e.g. large-scale stationary storage or even new technology fields such as electric aircraft. The development and design of new energy materials, electrodes, and cell designs significantly benefit from predictions and guidelines provided by computational approaches from atomistic to continuum scale. However, significant challenges remain for the predictive simulation of electrochemical energy storage. Particularly, schemes passing information from atomistic simulations on the material level to continuum simulations for cell-level performance predictions, and backward feeding information from continuum level to meso- and atomic-scale for relevant boundary and real-life cell cycling conditions. Moreover, novel cell technologies use materials with large volume expansion or solid electrolytes, rendering mechanical aspects critical for the performance and degradation of those systems. Therefore, thermo-electro-chemo-mechanical models and efficient numerical solvers are required for battery design and optimization. Finally, integrating batteries into the application and their monitoring and control requires computationally efficient yet accurate tools for state-of-charge and state-of-health estimation. Machine learning techniques promise significant advances in this field, yet superior performance and reliable control in critical applications are still to be demonstrated. This symposium addresses the development of computational tools from atomistic to continuum scale and their application for analyzing, designing, and monitoring current and next-generation batteries. Special emphasis is placed on the development of coupled mechanical-electrochemical models and implementation of multiscale modeling frameworks that can systematically predict properties of electrochemical devices during operation. Contributions may cover but are not limited to the design of improved materials, hierarchically structured materials, imaging, characterization, and modeling of 3D structures on multiple scales, process-structure-property relationships, advanced physics-based modeling approaches