Multi-scale thermal modeling is a critical approach for understanding and predicting thermal behavior in battery systems, from individual electrode particles to full battery packs. This technique enables engineers to analyze how heat generated at the micro-scale propagates through larger structures, ensuring safe and efficient operation. The challenge lies in bridging the gap between different length and time scales while maintaining computational efficiency. This article explores the methodologies for coupling micro-scale and macro-scale thermal simulations, the tools used, and their applications in battery design.

At the cell level, heat generation originates from electrochemical reactions, Joule heating, and entropic effects. These processes occur within the active materials, electrolytes, and interfaces of the battery. Micro-scale models focus on the behavior of electrode particles, where heat generation is influenced by local current density, particle morphology, and material properties. For example, lithium-ion intercalation and deintercalation in anode and cathode particles produce reversible and irreversible heat. Micro-scale models often employ finite element methods or discrete element methods to resolve temperature gradients within particles and at particle-electrolyte interfaces.

To link micro-scale heat generation to macro-scale thermal propagation, multi-scale modeling employs homogenization techniques or reduced-order models. Homogenization averages micro-scale properties to represent their collective behavior at the cell level. For instance, the effective thermal conductivity of an electrode layer can be derived by combining the properties of active materials, binders, and pores. Reduced-order models simplify micro-scale physics into empirical or semi-empirical equations, reducing computational cost while preserving accuracy. These models are then integrated into larger-scale simulations.

At the module or pack level, thermal propagation depends on cell arrangement, cooling strategies, and thermal interfaces between components. Macro-scale models simulate temperature distribution across multiple cells, accounting for conduction, convection, and radiation. The coupling between cell-level and pack-level models requires careful handling of boundary conditions and heat flux exchanges. For example, a cell’s surface temperature from a micro-scale simulation becomes an input for the pack-level model, which then calculates how adjacent cells influence each other’s thermal behavior.

Several software tools facilitate multi-scale thermal modeling. COMSOL Multiphysics is widely used for its ability to couple electrochemical and thermal physics across scales. Its built-in Lithium-Ion Battery interface allows users to simulate heat generation at the particle level while linking to larger geometries. ANSYS Fluent and Mechanical offer similar capabilities, with user-defined functions enabling custom coupling between scales. These tools support conjugate heat transfer simulations, where fluid cooling systems are modeled alongside solid components.

A common approach involves hierarchical modeling, where results from one scale inform the next. For example, a micro-scale model computes the heat generation rate of a single electrode particle under specific operating conditions. This data is then used to parameterize a cell-level model, which aggregates heat sources from all particles. The cell-level output, such as temperature distribution, feeds into a module-level simulation that evaluates thermal propagation across multiple cells. This hierarchical method balances accuracy and computational feasibility.

Another technique is concurrent multi-scale modeling, where micro- and macro-scale simulations run simultaneously with real-time data exchange. This approach is computationally intensive but captures dynamic interactions between scales. For instance, a concurrent model might simulate particle-level heat generation while simultaneously solving pack-level cooling effects, allowing feedback between scales. Tools like COMSOL’s LiveLink or ANSYS’s System Coupling enable such integrations.

Validation of multi-scale models is essential to ensure reliability. Experimental techniques such as infrared thermography or embedded temperature sensors provide data for comparing simulated and actual thermal behavior. Discrepancies often arise from assumptions in homogenization or neglected micro-scale effects, necessitating iterative refinement. For example, if a model underestimates pack-level temperatures, revisiting the micro-scale heat generation assumptions may reveal overlooked local hotspots.

Applications of multi-scale thermal modeling include cooling system optimization and thermal runaway prevention. By understanding how heat propagates from particles to packs, engineers can design more effective cooling plates, phase-change materials, or air/liquid flow paths. For thermal runaway studies, multi-scale models identify critical hotspots and propagation pathways, informing safety measures like thermal barriers or venting mechanisms.

In summary, multi-scale thermal modeling bridges the gap between micro-scale heat generation and macro-scale thermal propagation in battery systems. Through techniques like homogenization, reduced-order modeling, and hierarchical or concurrent coupling, engineers can simulate complex thermal interactions across scales. Tools like COMSOL and ANSYS provide the necessary computational frameworks, while experimental validation ensures accuracy. This approach is indispensable for designing safer, more efficient battery systems.