Electrochemical modeling serves as a critical tool for understanding and optimizing battery performance, yet several unresolved challenges hinder its predictive accuracy and practical applicability. These challenges span parameter uncertainty, interfacial complexities, and validation at scale, each presenting unique obstacles that require targeted research efforts. Addressing these gaps is essential for advancing battery technology and enabling more reliable simulations for real-world applications.

One of the most persistent issues in electrochemical modeling is parameter uncertainty. Battery models rely on numerous input parameters, such as diffusion coefficients, reaction rate constants, and ionic conductivities, which are often derived from experimental measurements or theoretical approximations. However, these parameters can vary significantly depending on measurement techniques, environmental conditions, and material batch inconsistencies. Small errors in parameter estimation can propagate through simulations, leading to substantial deviations from actual battery behavior. For instance, the diffusivity of lithium ions in electrode materials is sensitive to temperature and state of charge, yet many models assume fixed values for simplicity. Future research should focus on high-throughput experimental characterization combined with advanced parameter estimation algorithms, such as Bayesian inference, to quantify and reduce uncertainties. Additionally, developing standardized protocols for parameter measurement across different laboratories would improve consistency and model reliability.

Interfacial complexities present another major challenge in electrochemical modeling. Battery operation involves dynamic processes at multiple interfaces, including electrode-electrolyte boundaries, grain boundaries within active materials, and solid-electrolyte interphase (SEI) layers. These interfaces are often poorly understood due to their nanoscale heterogeneity and transient nature. For example, the SEI layer evolves during cycling, affecting both performance and degradation, but most models simplify it as a static, homogeneous film. Similarly, the charge transfer kinetics at electrode surfaces can vary locally due to surface defects or inhomogeneous electrolyte distribution. To address these issues, future work should integrate multi-scale modeling approaches that couple atomistic simulations with continuum models. Techniques like density functional theory (DFT) can provide insights into interfacial energetics, while mesoscale models can capture phase separation and morphology changes. Experimental techniques such as in-situ spectroscopy and high-resolution microscopy should be leveraged to validate these models and refine their assumptions.

Validation at scale remains a critical hurdle for electrochemical models. While many models perform well for single cells or simplified geometries, their accuracy often deteriorates when applied to large-format cells or battery packs. This scalability issue arises from neglected heterogeneities in temperature, current distribution, and aging effects across larger systems. For instance, localized heating or uneven pressure distribution in a multi-cell pack can lead to divergent behavior that is not captured by single-cell models. Moreover, the computational cost of simulating full-scale systems with high fidelity is often prohibitive. Future research should prioritize the development of reduced-order models that retain essential physics while minimizing computational overhead. Hybrid approaches that combine empirical data with physics-based models could also improve scalability. Large-scale validation campaigns, involving standardized testing protocols across industry and academia, would help identify gaps and refine modeling frameworks.

Another underexplored area is the integration of manufacturing variability into electrochemical models. Real-world batteries exhibit performance variations due to inconsistencies in electrode coating, calendaring, and assembly processes. These variations are rarely accounted for in simulations, which typically assume idealized geometries and uniform material properties. Incorporating stochastic elements into models, such as pore size distribution or electrode thickness variations, could provide more realistic predictions. Research should explore methods to quantify manufacturing tolerances and their impact on performance, enabling models to predict not only average behavior but also performance distributions across production batches.

The dynamic nature of battery degradation also poses challenges for electrochemical modeling. Most models focus on initial performance but struggle to predict long-term aging accurately. Degradation mechanisms such as lithium plating, particle cracking, and electrolyte decomposition involve coupled electrochemical, mechanical, and thermal effects that are difficult to capture comprehensively. Future efforts should emphasize the development of coupled degradation models that integrate these multi-physics phenomena. Accelerated aging experiments, combined with advanced diagnostics, could provide the data needed to parameterize and validate such models.

Finally, the lack of open, standardized benchmarking datasets hampers progress in electrochemical modeling. Many researchers validate their models against proprietary or limited experimental data, making it difficult to compare different approaches objectively. Establishing shared datasets encompassing diverse operating conditions, cell formats, and materials would facilitate more rigorous model evaluation. Collaborative initiatives between industry and academia could play a pivotal role in creating these resources.

Future research directions should prioritize the following areas: First, advancing multi-scale and multi-physics modeling frameworks to better capture interfacial phenomena and degradation mechanisms. Second, improving parameter identification methods through combined experimental and computational approaches. Third, developing scalable validation methodologies that bridge the gap between lab-scale models and real-world applications. Fourth, integrating manufacturing variability into simulations to enhance their predictive power for industrial use. Fifth, fostering collaboration to establish standardized benchmarking datasets and validation protocols.

By addressing these challenges, the field can move toward more robust and predictive electrochemical models, ultimately accelerating the development of next-generation batteries. The path forward requires close collaboration between theorists, experimentalists, and engineers to ensure that models are grounded in physical reality while remaining computationally tractable for practical applications.