Computational models play a critical role in predicting thermal runaway triggers and propagation in lithium-ion batteries, offering insights that are difficult to obtain through experimental methods alone. These models integrate electrochemical and thermal dynamics to simulate how localized overheating can escalate into catastrophic failure. By leveraging software tools like COMSOL Multiphysics and ANSYS Fluent, researchers and engineers can analyze the complex interplay between heat generation, chemical reactions, and material properties under various operating conditions.

Thermal runaway occurs when heat generation within a battery exceeds its ability to dissipate energy, leading to a self-sustaining reaction. The process typically begins with localized overheating due to internal short circuits, overcharging, mechanical damage, or external heating. Once initiated, exothermic reactions in the anode, cathode, and electrolyte accelerate, releasing more heat and flammable gases. Computational models simulate these stages by solving coupled electrochemical-thermal equations that describe the energy balance and reaction kinetics.

A key component of these models is the electrochemical sub-model, which calculates heat generation based on cell operation. The heat sources include ohmic heating from internal resistance, reversible entropic heating during charge-discharge, and irreversible heat from side reactions. The thermal sub-model then predicts temperature distribution across the cell by accounting for conduction, convection, and radiation. Coupling these sub-models allows the simulation to capture feedback loops where rising temperatures accelerate reactions, further increasing heat output.

Software tools like COMSOL Multiphysics enable the integration of these sub-models through partial differential equations. For example, COMSOL’s Lithium-Ion Battery interface couples the Doyle-Fuller-Newman (DFN) electrochemical model with heat transfer physics. This approach simulates how localized temperature spikes affect ion transport, reaction rates, and material decomposition. Similarly, ANSYS Fluent employs computational fluid dynamics (CFD) to model thermal propagation in battery packs, considering airflow and cooling system effects.

One critical aspect of these simulations is the inclusion of material decomposition reactions. As temperatures rise beyond safe limits, the solid-electrolyte interphase (SEI) layer on the anode breaks down, exposing the anode to further reactions with the electrolyte. Cathode materials like lithium nickel manganese cobalt oxide (NMC) release oxygen at high temperatures, fueling combustion. Electrolytes decompose into flammable hydrocarbons, while binders and separators melt or shrink, exacerbating internal shorts. Computational models parameterize these reactions using Arrhenius equations and experimentally derived activation energies to predict their onset temperatures and heat release rates.

Validation of these models relies on comparing simulated temperature profiles with experimental data from accelerating rate calorimetry (ARC) or differential scanning calorimetry (DSC). Studies have shown that models can accurately predict the sequence of exothermic reactions, such as SEI decomposition starting around 80-120°C, followed by anode-electrolyte reactions at 120-250°C, and cathode decomposition above 200°C. The models also reproduce the critical temperature threshold—typically 200-300°C—where thermal runaway becomes unstoppable.

Propagation of thermal runaway in multi-cell battery packs introduces additional complexity. Heat transfer between adjacent cells depends on their spacing, thermal insulation, and cooling mechanisms. Computational models simulate this by solving three-dimensional heat diffusion equations with boundary conditions representing pack geometry. For instance, a model might reveal that a 5°C temperature gradient between cells can delay propagation by several minutes, providing crucial time for safety systems to intervene. Simulations also evaluate the effectiveness of thermal barriers, phase-change materials, or liquid cooling in containing runaway events.

Advanced models incorporate gas generation and pressure dynamics, as venting of flammable gases often precedes catastrophic failure. By integrating fluid dynamics with electrochemical-thermal simulations, tools like ANSYS Fluent predict gas flow paths and ignition risks within battery enclosures. This is particularly relevant for electric vehicles, where confined spaces can lead to rapid pressure buildup.

Practical applications of these models include designing safer batteries and optimizing battery management systems (BMS). For example, simulations can identify high-risk regions within a cell, such as areas near current collectors where joule heating is concentrated. Manufacturers use these insights to modify electrode thickness, adjust porosity, or select more thermally stable materials. BMS algorithms leverage model predictions to set temperature thresholds for preemptive shutdown or activate cooling systems.

Despite their utility, computational models face challenges. Accurate parameterization requires extensive experimental data, and assumptions about material properties or reaction kinetics can introduce errors. Multiscale phenomena—from atomic-level reactions to pack-level heat transfer—are difficult to capture in a single simulation. Ongoing improvements focus on machine learning to refine parameters and high-performance computing to handle larger systems.

In summary, computational models for thermal runaway combine electrochemical and thermal physics to predict failure triggers and propagation pathways. Software tools like COMSOL and ANSYS enable detailed simulations that guide safer battery design and operation. By quantifying risks and evaluating mitigation strategies, these models are indispensable for advancing battery safety across industries.