Electrochemical modeling of solid-electrolyte interphase formation and growth provides critical insights into battery aging mechanisms, particularly in lithium-ion systems. The SEI is a passivation layer that forms on anode surfaces, primarily during initial cycles, as reduction products of electrolyte decomposition accumulate. While the SEI stabilizes the electrode-electrolyte interface, its continued growth contributes to irreversible capacity loss and increased impedance. Computational approaches enable the prediction of SEI evolution by capturing complex physicochemical interactions that govern its dynamics.

The formation of SEI begins with electron transfer reactions at the electrode surface, where electrolyte components such as carbonates, fluorinated compounds, or ethers undergo reduction. These reactions produce insoluble products like lithium carbonate, lithium fluoride, and polymeric species that precipitate onto the anode. Electrochemical models describe this process through coupled partial differential equations that account for mass transport, charge transfer kinetics, and phase nucleation. The initial reduction follows Butler-Volmer kinetics, with the reaction rate dependent on overpotential and local concentrations of reducible species. As the SEI grows, electron tunneling through the layer becomes rate-limiting, transitioning the growth mechanism from reaction-controlled to diffusion-controlled.

Transport limitations play a central role in SEI evolution. The developing layer acts as a resistive barrier to both lithium ions and solvent molecules. Models treat the SEI as a porous medium with tortuous pathways, where the effective diffusivity of species decreases as thickness increases. Concentration gradients across the SEI drive solvent diffusion toward the electrode, while lithium ion migration occurs in the opposite direction. The competition between these fluxes determines the growth rate. Simulations show that SEI thickening follows a parabolic relationship with time under diffusion-limited conditions, consistent with observed square-root time dependence in experimental data. The porosity and tortuosity of the SEI are key parameters, with lower porosity leading to stronger transport limitations and slower growth.

Mechanical stress effects arise from volume changes during SEI formation and lithium intercalation. The deposition of reduction products creates compressive stresses, while electrode expansion during lithiation induces tensile strains. Models incorporate elasticity and fracture mechanics to predict stress accumulation and crack formation. High stresses can lead to SEI rupture, exposing fresh electrode surfaces to further electrolyte decomposition. This self-amplifying process accelerates capacity fade. Stress-dependent models couple mechanical energy with electrochemical driving forces, showing that SEI growth rates increase when mechanical failure occurs. The balance between passivation and repair determines long-term stability.

Reaction pathways within the SEI involve multiple parallel processes. Primary reactions generate inorganic components near the electrode, while secondary reactions produce organic species closer to the electrolyte interface. The spatial distribution of these phases influences ionic conductivity and mechanical properties. Multi-layer models assign distinct transport and kinetic parameters to different SEI regions, capturing the gradient in composition. Side reactions involving trace water or acidic impurities introduce additional pathways, modeled through competitive kinetics. The relative rates of these parallel reactions determine SEI composition and its subsequent evolution.

Capacity fade predictions integrate SEI growth with active lithium loss. Each cycle consumes lithium ions irreversibly trapped in the SEI, reducing available charge carriers. Models track the cumulative lithium loss by coupling SEI thickness with Faradaic efficiency. The relationship is nonlinear due to the increasing resistance of thicker SEI layers, which reduces the effective driving force for further growth. Simulations demonstrate that capacity fade accelerates during early cycles before stabilizing as the SEI reaches a self-limiting thickness. However, mechanical degradation can disrupt this equilibrium, leading to renewed growth phases.

Impedance rise stems from increasing resistance to lithium ion transport through the SEI. Electrochemical impedance spectroscopy models represent the SEI as a distributed circuit element with resistance and capacitance parameters that evolve with thickness and composition. The charge transfer resistance at the electrode-SEI interface grows as electron tunneling becomes less efficient. Simultaneously, the SEI bulk resistance increases due to longer ion transport paths. Models quantify these contributions through transmission line analogs, predicting impedance spectra that match experimental observations. The high-frequency semicircle in Nyquist plots corresponds to SEI resistance, while low-frequency features reflect diffusion limitations.

Temperature effects are incorporated through Arrhenius relationships for kinetic and transport parameters. Elevated temperatures accelerate both SEI formation and electrolyte decomposition, but also enhance ion mobility, creating competing influences on growth rate. Models reveal an optimal temperature range where SEI stability is maximized. Below this range, lithium plating exacerbates SEI formation, while above it, thermal degradation dominates. Spatial temperature gradients, common in large-format cells, induce non-uniform SEI growth that models capture through coupled thermal-electrochemical simulations.

Multi-scale approaches bridge atomistic details with continuum behavior. Ab initio calculations provide activation energies for reduction reactions, while molecular dynamics simulations estimate transport properties in SEI components. These parameters inform macroscopic models that predict cell-level performance degradation. The hierarchical framework enables efficient simulation of SEI evolution over long timescales without sacrificing mechanistic accuracy.

Validation against experimental data confirms model predictions of capacity fade and impedance trends. Parametric studies identify dominant degradation modes under different operating conditions, guiding battery management strategies. Sensitivity analyses reveal that electrolyte composition and cycling protocols have stronger influence on SEI growth than minor variations in electrode morphology. This insight informs design choices that prioritize interfacial stability.

Future model development will incorporate more sophisticated descriptions of SEI chemistry, including potential-dependent phase transformations and the role of nanoscale heterogeneities. Coupling with mechanical degradation models will improve predictions of aging under dynamic loading. As computational power increases, real-time SEI monitoring based on electrochemical models may become feasible, enabling adaptive control systems that mitigate degradation. The continued refinement of these tools will enhance battery durability and safety across applications.