Electrochemical modeling is a critical tool for understanding and optimizing battery systems, particularly for emerging technologies like aqueous zinc-ion (Zn-ion) and sodium-ion (Na-ion) batteries. These systems present unique challenges compared to traditional lithium-ion batteries, requiring specialized modeling approaches to account for side reactions, solvent effects, and ion transport mechanisms. While lithium-ion models provide a foundational framework, adaptations are necessary to address the distinct behavior of aqueous and non-lithium systems.

Aqueous batteries, such as Zn-ion systems, operate in water-based electrolytes, which introduce complexities absent in organic solvent-based lithium-ion batteries. The presence of water leads to side reactions like hydrogen evolution, oxygen reduction, and corrosion, which degrade electrode materials and reduce cycle life. Electrochemical models for these systems must incorporate these parasitic reactions to accurately predict performance. For example, the hydrogen evolution reaction at the zinc anode can be modeled using Butler-Volmer kinetics, with overpotentials adjusted to reflect the aqueous environment. Similarly, pH changes in the electrolyte due to side reactions must be tracked, as they influence reaction rates and material stability.

Solvent effects in aqueous batteries also impact ion transport and interfacial kinetics. Water’s high dielectric constant and solvation properties alter ion mobility and charge transfer rates compared to organic solvents. Models must account for these differences by adjusting transport parameters like diffusion coefficients and ionic conductivity. The formation of solid-electrolyte interphases (SEI) in aqueous systems is less common than in lithium-ion batteries, but passivation layers can still form and must be included in degradation models.

Non-lithium systems, such as Na-ion batteries, share some similarities with lithium-ion batteries but require modifications to address larger ion sizes and different electrochemical potentials. Sodium ions have a larger ionic radius than lithium ions, leading to slower diffusion kinetics in electrode materials. This necessitates adjustments to diffusion models, often using lower diffusion coefficients derived from experimental data. The electrochemical potential of sodium also differs, affecting open-circuit voltage profiles and charge/discharge behavior. Models must incorporate these variations to accurately simulate cell performance.

A key challenge in modeling both aqueous and non-lithium systems is the lack of extensive experimental data compared to lithium-ion batteries. Parameterization of models often relies on limited datasets, introducing uncertainties. For example, kinetic parameters for sodium-ion intercalation reactions are less established, requiring sensitivity analyses to assess model robustness. Similarly, the thermodynamics of side reactions in aqueous systems are not always well-characterized, necessitating approximations in model development.

Thermal effects are another consideration. Aqueous batteries are less prone to thermal runaway than lithium-ion systems but still experience temperature-dependent behavior. Models must include thermal coupling to capture the impact of temperature on reaction rates and transport phenomena. In Na-ion batteries, thermal effects can influence phase transitions in electrode materials, which must be accounted for in degradation models.

Comparative frameworks highlight the differences between lithium-ion and alternative battery models. Lithium-ion models often assume a single dominant charge carrier, while aqueous systems require multi-ion transport models to account for proton and hydroxide ion movement. Similarly, lithium-ion models typically focus on intercalation reactions, whereas aqueous systems must include dissolution-precipitation mechanisms for metals like zinc. These differences necessitate modular modeling approaches, where sub-models for side reactions or ion transport can be added or removed as needed.

Mechanical effects also play a role, particularly in aqueous systems where dendritic growth can occur. Models for zinc deposition must include morphological changes to predict dendrite formation and its impact on cell shorting. In Na-ion batteries, volume changes in electrode materials during cycling can lead to mechanical stress, requiring coupled electrochemical-mechanical models to predict long-term performance.

The choice of modeling scale is another consideration. Atomistic models can provide insights into ion solvation and interfacial behavior but are computationally expensive for cell-level simulations. Continuum models are more practical for engineering applications but may overlook molecular-scale phenomena. Multi-scale approaches, combining atomistic and continuum methods, are increasingly used to bridge this gap.

Validation of models for aqueous and non-lithium systems is challenging due to the dynamic nature of side reactions and material evolution. In-situ characterization techniques, such as electrochemical impedance spectroscopy, are essential for refining model parameters. However, the lack of standardized testing protocols for these emerging technologies complicates cross-study comparisons.

Despite these challenges, electrochemical modeling remains indispensable for advancing aqueous and non-lithium batteries. By adapting lithium-ion frameworks to address unique phenomena like side reactions and solvent effects, researchers can accelerate the development of these promising technologies. Future work should focus on improving parameterization, integrating multi-physics effects, and validating models against experimental data to enhance predictive accuracy.