Physics-based degradation modeling for lithium-ion batteries provides a framework to understand and predict capacity fade and impedance growth over time. These models rely on electrochemical principles to describe the underlying mechanisms that contribute to battery aging. By capturing the interactions between materials, operating conditions, and electrochemical processes, such models enable better battery design, management, and lifetime prediction.

The degradation of lithium-ion batteries is driven by multiple mechanisms, each contributing to performance loss. The primary mechanisms include solid-electrolyte interphase (SEI) growth, lithium plating, particle cracking, and electrolyte decomposition. These processes are influenced by factors such as temperature, charge/discharge rates, and depth of discharge (DOD).

Solid-electrolyte interphase (SEI) growth is one of the most significant contributors to capacity fade. The SEI is a passivation layer that forms on the anode surface due to electrolyte reduction during initial cycles. While it stabilizes the electrode-electrolyte interface, continued SEI growth consumes active lithium ions and increases cell impedance. The growth rate depends on temperature and cycling conditions, with higher temperatures accelerating decomposition reactions. SEI growth is often modeled using a diffusion-limited process, where the SEI thickness increases with the square root of time. The associated capacity loss is proportional to the consumed lithium inventory.

Lithium plating occurs when lithium ions are reduced to metallic lithium on the anode surface instead of intercalating into the active material. This phenomenon is prevalent at high charging rates, low temperatures, or when the anode potential drops below 0 V vs. Li/Li+. Plating leads to irreversible capacity loss and can cause dendrite formation, increasing the risk of internal short circuits. Models describe lithium plating using kinetic equations that account for overpotential, exchange current density, and temperature effects. The plated lithium may react with the electrolyte, further contributing to SEI growth.

Particle cracking in electrode materials results from mechanical stresses induced by repeated lithiation and delithiation. Active material particles, particularly in high-capacity electrodes like silicon or nickel-rich cathodes, undergo volume changes that generate internal stresses. Over time, these stresses cause particle fracture, leading to loss of electrical contact and increased impedance. Mechanical degradation models incorporate stress-strain relationships, fracture mechanics, and particle morphology. Coupling these with electrochemical models allows prediction of how cracking impacts overall cell performance.

Electrolyte decomposition occurs at both electrodes but is more pronounced at high voltages or elevated temperatures. The breakdown of electrolyte solvents and salts produces gaseous byproducts and resistive layers, increasing cell impedance. Electrolyte degradation is often modeled using reaction kinetics, where decomposition rates depend on electrode potential and temperature. The loss of active electrolyte also reduces ionic conductivity, further impairing performance.

Operating conditions play a critical role in degradation progression. Temperature has a strong influence on reaction kinetics, with higher temperatures accelerating SEI growth and electrolyte decomposition but also mitigating lithium plating by improving ion transport. Charge/discharge rates affect overpotentials, with high currents promoting lithium plating and mechanical degradation. Depth of discharge determines the extent of electrode volume changes, influencing particle cracking and SEI stability. Models incorporate these factors through empirical or physics-based relationships to capture their impact on aging.

Mathematically, degradation models integrate these mechanisms into continuum-scale frameworks such as the pseudo-two-dimensional (P2D) model. The P2D model describes lithium transport in electrodes and electrolyte, coupled with charge transfer kinetics. Degradation submodels are added to account for SEI growth, lithium plating, and other mechanisms. For example, SEI growth may be represented by a boundary condition that consumes lithium ions, while particle cracking modifies electrode porosity and effective transport properties.

Parameterization of these models remains a key challenge. Many degradation mechanisms depend on material-specific properties that are difficult to measure directly, such as SEI conductivity or fracture toughness of particles. Calibration requires extensive experimental data, including long-term cycling tests, impedance spectroscopy, and post-mortem analysis. Even with sufficient data, the interplay between mechanisms introduces complexity in isolating individual contributions.

Computational complexity is another limitation. High-fidelity degradation models require solving coupled nonlinear partial differential equations, which are computationally expensive. Reduced-order models and surrogate modeling techniques are often employed to balance accuracy and simulation speed. Machine learning approaches are increasingly used to accelerate predictions by training on high-fidelity simulation data.

Validation of degradation models relies on comparison with experimental data. Cycling tests under controlled conditions provide capacity fade and impedance growth trends, while advanced characterization techniques like scanning electron microscopy (SEM) and X-ray diffraction (XRD) reveal structural changes. Differential voltage analysis (DVA) and incremental capacity analysis (ICA) are also used to identify degradation modes from voltage profiles.

Despite these challenges, physics-based degradation modeling offers valuable insights for improving battery longevity. By understanding how different mechanisms interact under varying conditions, researchers can optimize electrode materials, cell designs, and operating strategies to minimize degradation. Future advancements in computational power and multi-scale modeling techniques will further enhance predictive accuracy, supporting the development of more durable and efficient energy storage systems.

In summary, physics-based degradation modeling provides a systematic approach to analyze lithium-ion battery aging. By incorporating electrochemical mechanisms such as SEI growth, lithium plating, particle cracking, and electrolyte decomposition, these models enable precise lifetime predictions and performance optimization. While challenges remain in parameterization and computational efficiency, ongoing research continues to refine these tools, paving the way for next-generation battery technologies.