Abstract

Defense and energy applications ubiquitously involve multiscale and multiphysics systems. Their accurate modeling, critical to achieve superior performances and optimized designs and control strategies, has challenged generations of computational physicists due to the mathematical and numerical complexities involved in the development of their computable representations. One of the fundamental challenges associated with modeling multiscale processes is the development of rigorous models at the scale of interest (system-scale), which is typically much larger than the scale at which the physics is best understood (fine-scale). Coarse-graining techniques are a suite of mathematical strategies that allow one to perform rigorous scale translation, while bounding a priori upscaling errors. Yet, they require substantial time and mathematical expertise to use. This is due to the number of analytical manipulations and rigorous approximations (e.g., series expansions) involved during model development that quickly become analytically intractable for systems of realistic complexities (e.g., systems with large numbers of interacting physics, nested scales, and chemical species). In addition, their direct application and deployment in practical problems may sometime feel obscure to nonspecialists. These elements often lead practitioners to select simplified/heuristic models and representations whose accuracy cannot be established across a wide range of material parameters and operating conditions in favor of more advanced physics-based multiscale formulations.

Electrochemical and thermal modeling of battery systems share the abovementioned complexities. In this talk, I will present a unified framework, developed within the group and with collaborators over the past 10 years, that self-consistently integrates upscaling theory by multiple-scale expansions, numeric and automated deductive symbolic computing, and more recently control, for multiscale modeling of batteries. We will focus on two separate applications: (i) multiscale models of electrochemical transport in battery electrodes [1,2], their parametrization from microstructural information using ML [3,4], and their initial deployment in control algorithms [5]; (ii) thermal runaway in battery packs [6,7].

We will conclude with an outlook on how the integration of automated symbolic deduction and numeric computing to societally relevant applications allows to go beyond human-centered limitations and to accelerate model development in multiscale multiphysics processes without compromising model interpretability and accuracy.

Bio

Dr. Battiato is Associate Professor of Energy Science and Engineering at Stanford University and leads the Multiscale Physics in Energy Systems Laboratory. Dr. Battiato’s research focuses on understanding, modeling, and predicting complex multiscale multiphysics systems with cross-cutting applications in the energy landscape ranging from electrochemical energy storage to CO₂ sequestration and H₂ storage in the subsurface. She uses a combination of rigorous mathematical theories, numerical computing and symbolic deduction to develop advanced multiscale multiphysics models. She received the DOE Young Investigator award in Basic Energy Sciences for her innovative work on multiscale models in reactive porous media, has been awarded the Frederick Emmons Terman Fellowship at Stanford University, and the 2026 InterPore Award for Porous Media Research. She is also the 2026 elected vice-chair and 2028 chair of the Gordon Research Conference for Flow and Transport in Permeable media alongside Dr. Inga Berre.

References

[1] H. Arunachalam, Onori, S., Battiato, I., ‘On veracity of lithium-ion battery macroscopic models’, J. Electrochem. Soc. (2015).

[2] Lombardo Pontillo, A.; Marcato, A.; Marchisio, D.; Boccardo, G.; Battiato, I. Math for Engineering: Physics-based Reduced Order Models for Electrochemical Transport in Lithium-Ion Batteries, To be submitted.

[3] R. M. Weber, S. Korneev, I. Battiato, 'Homogenization-Informed Convolutional Neural Networks for Estimation of Li-ion Battery Effective Properties’, Transport in Porous Media, Volume 145, pages 527–548, (2022)

[4] Weber, R.; Korneev, S.; Battiato, I., “Labeled Image Dataset of Generated Porous Electrode Microstructures and Calculated Transport Parameters for Neural Network Training”, Mendeley Data, V1, doi: 10.17632/mgmxv5tjt2.1, (2022).

[5] A. A. Lodge, A. L. Pontillo, F. S.J. Hoekstra, R. Medina, S. Wilkins, I. Battiato, Health-Aware Fast Charging Using Homogenized Model with Heterogeneous Internal State Reconstruction, Accepted, International Federation of Automatic Control (IFAC), Busan, South Korea, IFAC 2026.

[6] Pietrzyk, K., Bucci, G., Behandish, M., Battiato, I., ‘Automated Upscaling via Symbolic Computing for Thermal Runaway Analysis in Li-ion Battery Modules’, J. Comp. Science, 74, 102134 (2023).

[7] Z. Ping, K. M. Pietrzyk and I. Battiato, “Thermal runaway in battery cells: Guiding principles for continuum-scale modeling” To be submitted to J. Electrochem. Soc. (2025)