Battery degradation is a complex phenomenon influenced by multiple interdependent factors, with electrochemical and thermal processes playing a central role. Among these, the coupling between electrochemical reactions and heat generation is particularly critical, as it creates feedback loops that accelerate aging. Side reactions such as solid electrolyte interphase (SEI) growth, lithium plating, and electrolyte decomposition not only consume active lithium but also generate heat, further exacerbating degradation. Understanding these coupled mechanisms requires advanced modeling approaches that integrate electrochemical kinetics with thermal dynamics.

Coupled electrochemical-thermal aging models solve partial differential equations (PDEs) that describe mass transport, charge conservation, and heat generation simultaneously. The electrochemical domain is governed by porous electrode theory, which includes lithium-ion diffusion in electrodes and electrolyte, charge transfer kinetics, and Butler-Volmer reaction rates. The thermal domain accounts for heat accumulation due to ohmic losses, entropic changes, and exothermic side reactions. The coupling arises because temperature influences reaction rates, ionic conductivity, and diffusivity, while the heat generated from these reactions in turn affects local temperature distributions.

Numerical methods are essential for solving these coupled PDEs due to their nonlinear and stiff nature. Finite element methods (FEM) and finite volume methods (FVM) are commonly employed, discretizing the battery geometry into computational grids where governing equations are solved iteratively. Operator splitting techniques are often used to handle the multiphysics nature of the problem, solving electrochemical and thermal subsystems sequentially within each time step. Implicit time-stepping schemes improve stability, especially when dealing with fast transients during high-current operation. Recent advances include adaptive mesh refinement to capture steep thermal gradients near current collectors and reduced-order modeling techniques to enable real-time simulations for battery management systems.

A key challenge in these models is accurately capturing the heat contribution from side reactions. SEI growth, for instance, is a major source of capacity fade and heat generation. The SEI layer forms through parasitic reactions between the anode and electrolyte, consuming lithium ions and producing heat. Over time, the SEI thickens, increasing cell impedance and generating additional joule heating. Models incorporate Arrhenius-type rate equations to describe temperature-dependent SEI growth, often calibrated using accelerated aging tests. Similarly, lithium plating during fast charging is another exothermic process that can lead to rapid degradation and safety risks.

Experimental validation of these models relies on calorimetry and in-situ measurements. Isothermal microcalorimetry quantifies heat flow from side reactions under controlled conditions, providing data to refine model parameters. Accelerating rate calorimetry (ARC) measures self-heating rates during thermal runaway, helping validate worst-case scenarios. Infrared thermography maps surface temperature distributions, while embedded microthermocouples or fiber-optic sensors provide internal temperature profiles. These measurements confirm whether the model accurately predicts localized hot spots and thermal propagation.

Applications of coupled electrochemical-thermal aging models are wide-ranging. In fast-charging optimization, simulations identify safe current limits that minimize lithium plating and SEI growth while preventing excessive temperature rise. By analyzing trade-offs between charging speed and degradation, these models guide the development of adaptive charging protocols that adjust rates based on real-time thermal feedback. Another critical application is thermal runaway prevention. Models predict how localized overheating from internal short circuits or mechanical abuse propagates through the cell, informing the design of thermal barriers and cooling systems.

The integration of degradation models with battery management systems (BMS) is an emerging trend. Reduced-order versions of coupled models run in real-time on BMS hardware, enabling predictive state-of-health (SOH) estimation and adaptive thermal management. For example, if the model predicts elevated SEI growth due to high temperatures, the BMS can preemptively reduce load current or activate cooling. Such capabilities are especially valuable in electric vehicles and grid storage, where operational conditions vary widely.

Despite their utility, challenges remain in improving the accuracy and computational efficiency of these models. Parameterization requires extensive experimental data, and uncertainties in material properties or reaction mechanisms can lead to prediction errors. Multiscale approaches that link atomistic simulations with continuum models are being explored to better capture fundamental degradation processes. Additionally, machine learning techniques are increasingly used to augment physics-based models, combining first-principles knowledge with data-driven corrections.

In summary, coupled electrochemical-thermal aging models provide a powerful framework for understanding and mitigating battery degradation. By capturing the interplay between electrochemical reactions and heat generation, they enable the design of safer, longer-lasting energy storage systems. Continued advancements in numerical methods, experimental validation, and real-time implementation will further enhance their role in battery development and operation.