Thermal runaway propagation in battery packs represents a critical safety challenge in energy storage systems, particularly in high-density applications like electric vehicles and grid storage. The phenomenon occurs when a single cell enters thermal runaway, generating sufficient heat to trigger adjacent cells, leading to cascading failure. Understanding and modeling this process requires analysis of exothermic reactions, heat transfer mechanisms, and material failure thresholds.

The initiation of thermal runaway begins with internal exothermic reactions within a lithium-ion cell. When temperatures exceed 80-120°C, the solid electrolyte interphase layer decomposes, followed by reactions between the anode and electrolyte at 120-200°C. The cathode becomes unstable at higher temperatures, with lithium metal oxide cathodes releasing oxygen around 200-250°C, which further accelerates exothermic processes. These reactions can generate heat at rates exceeding 1000 W per cell, with temperatures surpassing 800°C in severe cases.

Heat transfer between cells occurs through three primary mechanisms: conduction through electrical connections and structural components, convection via gases or liquids in the pack, and radiation at extreme temperatures. The propagation speed depends on the pack design, with tightly packed modules exhibiting faster thermal runaway spread due to reduced heat dissipation paths. Spacing between cells, thermal interface materials, and module housing composition significantly influence heat transfer rates.

Modeling thermal runaway propagation employs several computational approaches. Lumped-parameter models simplify cells as single nodes with uniform temperature, using energy balance equations to track heat generation and dissipation. These models compute heat flow between adjacent cells using thermal resistances and capacitances derived from material properties. While computationally efficient, they lack spatial resolution for complex pack geometries.

Computational fluid dynamics models provide higher fidelity by solving Navier-Stokes equations coupled with energy transport. CFD captures three-dimensional heat distribution, gas flow patterns, and pressure buildup within battery packs. These models require detailed input parameters including anisotropic thermal conductivities of cell components, temperature-dependent reaction kinetics, and venting characteristics. The computational intensity limits full-pack simulations to high-performance computing systems.

Multiphysics approaches combine electrochemical-thermal models with mechanical deformation analysis. These simulate how internal pressure buildup leads to cell venting or rupture, altering subsequent heat transfer paths. The models incorporate temperature-dependent material properties, such as the thermal conductivity of separators that decreases significantly near melting points, creating localized hot spots.

Critical thresholds in propagation modeling include the ignition temperature of adjacent cells, typically between 150-200°C for lithium-ion chemistries, and the heat transfer rate required to overcome the cooling capacity of the pack design. Time-to-propagation metrics help evaluate design safety margins, with typical values ranging from seconds to minutes depending on cell spacing and thermal barriers.

Material properties significantly impact model accuracy. Key parameters include the thermal conductivity of cell components: approximately 1-2 W/mK radial conductivity for cylindrical cells, with higher axial conductivity through current collectors. Heat capacity values range from 800-1200 J/kgK for cell assemblies. These values change during thermal runaway as materials decompose and gases form.

Mitigation strategies informed by modeling focus on interrupting propagation pathways. Thermal barriers with low conductivity materials like aerogels or phase change materials can absorb sufficient heat to prevent adjacent cell ignition. Modeling guides optimal placement and thickness of these barriers by quantifying required thermal resistance values. For example, a 5mm silica aerogel layer can reduce heat flux to adjacent cells by over 60%.

Venting channel design represents another modeled mitigation approach. Properly sized and positioned vents prevent pressure buildup that would otherwise force hot gases toward neighboring cells. Models optimize vent locations by simulating gas flow paths and temperature distributions during cell rupture. Directional venting designs that channel gases away from adjacent cells show particular effectiveness in simulations.

Cell arrangement studies using modeling reveal that hexagonal packing provides better heat dissipation than rectangular arrays in some configurations. However, the optimal geometry depends on cooling system design and module orientation. Models compare temperature distributions across different arrangements to identify configurations that delay propagation longest.

Advanced modeling techniques now incorporate probabilistic methods to account for manufacturing variations in cells. These simulations analyze how differences in internal resistance, thickness variations, or electrolyte distribution affect propagation likelihood. Results inform quality control standards and safety factor determinations.

Validation of thermal runaway models remains challenging due to the destructive nature of tests, but established models achieve reasonable agreement with experimental data for temperature profiles and propagation sequences. Ongoing improvements focus on better capturing gas generation effects and the transition from convective to radiative heat transfer at extreme temperatures.

The development of standardized modeling frameworks enables comparison of different battery systems and designs. These frameworks establish consistent approaches for input parameter definition, boundary condition specification, and results interpretation across the industry. Such standardization supports safety certification processes and design optimization efforts.

Future modeling directions include integration with production quality data to predict propagation risks based on manufacturing parameters, and coupling with battery management systems for real-time risk assessment. The increasing computational power available enables more detailed simulations of full-scale battery packs under diverse failure scenarios.

Understanding thermal runaway propagation through modeling provides essential insights for safer battery pack design without requiring exhaustive physical testing. The computational tools continue to evolve, offering increasingly accurate predictions that balance safety requirements with energy density and cost objectives in battery system development.