Relevance of Classical Models and Simulation Approaches for Battery Digital Twins

Digital twins are virtual replicas of physical systems updated real time and are increasingly vital for complex systems like batteries. Being electrochemical black boxes that degrade with use, batteries benefit from digital twins for monitoring, predictive maintenance, and optimization. Data-driven models are popular for digital twins due to their adaptability, but are limited by dependence on large datasets, weak interpretability, and poor generalization. In contrast, physics-based approaches such as Pseudo-2D (P2D) models offer higher accuracy, interpretability, and require less operational data. These models can be packaged and deployed across platforms using the Functional Mock-up Interface (FMI) and Functional Mock-up Units (FMUs). Through FMU deployment, this paper illustrates the challenges and opportunities in balancing model depth and scale across battery chemistries, as well as the robustness and efficiency of numerical methods across spatial and temporal scales. In addition, the paper highlights how classical models already integrate data-driven elements, such as empirical fits for open-circuit voltage and electrolyte properties. The most effective digital twin strategies for batteries are therefore hybrid models that combine the rigor of physics-based methods with the flexibility of data-driven tools. Such interpretable, scalable, and fast models are essential for advancing energy storage and enabling real-time control in diverse applications.