Multiscale simulation approaches for understanding thermal runaway in battery packs represent a critical tool for improving battery safety. These methods integrate phenomena across different length and time scales, from atomic-level material decomposition to pack-level thermal propagation. The approach combines quantum calculations, electrochemical-thermal models, and computational fluid dynamics to predict thermal runaway behavior with high fidelity.

At the quantum scale, simulations focus on material decomposition pathways that initiate thermal runaway. Density functional theory calculations predict the stability of electrode materials and electrolytes under thermal stress. For example, the decomposition of lithium cobalt oxide cathodes releases oxygen at temperatures above 200°C, while organic carbonate electrolytes begin breaking down near 120°C. These exothermic reactions contribute to heat generation at the cell level. Quantum-scale models provide activation energies and reaction rates that feed into higher-scale simulations.

Cell-level models couple electrochemical reactions with heat generation and transport. Pseudo-two-dimensional models solve lithium-ion concentration, potential distribution, and heat generation across electrodes and separators. Heat sources include joule heating, entropy changes, and chemical reactions. The thermal model accounts for conduction through solid components and convection at interfaces. Case studies show that localized hot spots exceeding 250°C can trigger cascading decomposition reactions. Abuse conditions such as nail penetration or overcharging accelerate these processes by creating internal short circuits.

At the pack level, computational fluid dynamics models simulate heat propagation between cells. These models solve mass, momentum, and energy conservation equations for air or coolant flow around battery modules. The interaction between neighboring cells depends on spacing, cooling design, and thermal insulation. For example, a study comparing parallel and series cell arrangements demonstrated that tight packing accelerates thermal runaway propagation due to reduced heat dissipation. Validation against experimental data shows that simulations can predict propagation speeds within 15% of measured values.

Coupling between electrochemical-thermal models and fluid dynamics requires iterative data exchange. The cell model provides heat generation rates to the fluid solver, which returns updated temperature distributions. Reduced-order models improve computational efficiency by approximating complex electrochemical processes with empirical correlations. A case study on a 12-cell module demonstrated that full coupling improves temperature prediction accuracy by 22% compared to one-way coupling.

Experimental validation involves comparing simulation results with abuse testing data. Nail penetration tests on lithium-ion cells show that models can reproduce voltage drop, temperature rise, and gas venting timing within experimental uncertainty. For pack-level tests, simulations predict the sequence of cell failures and maximum temperatures observed in thermal runaway propagation experiments. Discrepancies often arise from uncertainties in material properties and boundary conditions, highlighting the need for high-fidelity input data.

Recent advances include machine learning techniques to accelerate multiscale simulations. Neural networks trained on quantum calculations can predict material decomposition kinetics without repeated DFT computations. At the pack level, data-driven models reduce the computational cost of fluid dynamics simulations while preserving accuracy. These methods enable faster exploration of design parameters for safer battery systems.

Challenges remain in fully capturing all relevant physics across scales. Gas generation and ejection during thermal runaway affect pack-level heat transfer but are difficult to model precisely. Mechanical deformation of cells during failure also introduces uncertainties. Future work may integrate mechanical modeling with existing electrochemical-thermal-fluid approaches.

Multiscale simulations provide insights that guide safety improvements. Designs with thermal barriers between cells, optimized cooling channels, and stable electrode materials can delay or prevent thermal runaway propagation. Simulation tools enable virtual testing of these strategies before physical prototyping, reducing development time and cost. As battery energy densities increase, these modeling approaches become even more critical for ensuring safe operation under diverse conditions.

The integration of quantum-to-pack scale modeling represents a comprehensive framework for thermal runaway analysis. By combining fundamental material properties with system-level thermal management, these simulations advance the understanding of battery failure mechanisms and inform safer battery pack designs. Continued refinement of multiscale methods will further enhance their predictive capability and utility in battery development.