Coupled electro-thermal aging models are critical for understanding the long-term performance and reliability of high-performance batteries, particularly under demanding conditions such as fast charging or operation in extreme environments. These models integrate electrochemical behavior, thermal dynamics, and degradation mechanisms to predict how batteries age over time. The interplay between Joule heating, thermal gradients, and degradation pathways is complex, requiring multi-physics frameworks to capture the coupled effects accurately.

At the core of these models is the relationship between heat generation and electrochemical processes. During charge and discharge, resistive losses due to ion transport and charge transfer reactions generate heat, known as Joule heating. This heat raises the temperature of the battery, which in turn affects reaction kinetics, ion diffusivity, and material stability. Non-uniform heat distribution creates thermal gradients, leading to localized stress and accelerated degradation in certain regions of the cell. For example, higher temperatures near the current collectors can exacerbate solid-electrolyte interphase (SEI) growth, while cooler regions may experience lithium plating.

Multi-physics frameworks combine electrochemical models, such as the pseudo-two-dimensional (P2D) model, with thermal models to simulate these interactions. The P2D model accounts for lithium-ion transport in the electrolyte and solid phases, charge transfer kinetics, and potential distributions. Coupled with a thermal model, it predicts temperature evolution based on heat generation from electrochemical reactions and external cooling conditions. Aging laws are then incorporated to quantify degradation mechanisms such as SEI growth, lithium plating, particle cracking, and electrolyte decomposition. These laws are often empirically derived from experimental data or based on first-principles theories.

One common approach is to use Arrhenius-type equations to model temperature-dependent degradation rates. For instance, SEI growth is typically modeled as a diffusion-limited process with an activation energy that dictates its temperature sensitivity. Mechanical degradation, such as particle fracture due to repeated lithiation and delithiation, can be modeled using stress-strain relationships coupled with crack propagation theories. These models require inputs such as material properties, operating conditions, and cell geometry to predict spatially resolved degradation.

Accelerated aging tests under extreme temperatures are essential for validating these models and understanding real-world performance limits. Tests often involve cycling batteries at high C-rates or storing them at elevated temperatures to induce rapid degradation. For example, cycling at 45°C can accelerate SEI growth compared to room temperature, while sub-zero temperatures increase the risk of lithium plating during fast charging. Data from these tests help refine model parameters and reveal failure modes that may not be apparent under normal conditions.

The implications for fast-charging applications are significant. Fast charging increases Joule heating and can create steep thermal gradients, leading to heterogeneous aging. Models predict that repeated fast charging at high ambient temperatures can cause premature capacity fade due to accelerated SEI growth and lithium plating. Conversely, low-temperature fast charging may lead to metallic lithium deposition, which poses safety risks. Multi-physics simulations can optimize charging protocols by balancing speed with temperature management, such as preheating the battery in cold environments or modulating the current to limit peak temperatures.

Advanced frameworks also incorporate machine learning to enhance predictive accuracy. By training algorithms on large datasets from accelerated aging tests, models can identify patterns and correlations that traditional physics-based approaches might miss. Hybrid models combine the interpretability of physical laws with the adaptability of data-driven methods, improving predictions for complex aging scenarios.

Challenges remain in fully capturing all degradation pathways and their interactions. For example, the coupling between mechanical stress and electrochemical reactions is not yet fully understood, particularly in silicon or lithium-metal anodes that undergo large volume changes. Additionally, long-term validation under real-world conditions is necessary to ensure model reliability.

In summary, coupled electro-thermal aging models provide a powerful tool for designing durable high-performance batteries. By integrating electrochemical, thermal, and degradation physics, these models enable the development of safer and more efficient energy storage systems, particularly for fast-charging applications. Continued advancements in multi-physics modeling and experimental validation will further enhance their predictive capabilities and practical utility.