Relevance of classical models/approaches for Battery Digital Twins

Digital twins are virtual replicas of physical systems updated in real-time and becoming vital in modern engineering, especially for complex systems like batteries. Batteries, being electrochemical black boxes that degrade over time, benefit greatly from digital twins for real-time monitoring, predictive maintenance, and performance optimization. This paper explores various modeling approaches that underpin battery digital twins, including Equivalent Circuit Models (ECMs), physics-based electrochemical models, and data-driven or hybrid methods. While data-driven models offer adaptability, they face limitations due to data dependency, poor interpretability, and generalization issues. In contrast, physics-based models such as the Single Particle Model (SPM) and Pseudo-2D (P2D) models provide high accuracy, physical interpretability, and require less operational data.The paper also discusses the use of Functional Mock-up Interface (FMI) and Functional Mock-up Units (FMUs) to package and deploy these models across platforms. Comparative results from different numerical schemes like finite difference and Galerkin methods are presented, showing trade-offs in accuracy and computational efficiency. Advanced modeling approaches for phase-separating chemistries like LFP are explored using phase-field methods. Importantly, the paper highlights how classical models already integrate data-driven elements, such as empirical fits for open-circuit voltage and electrolyte properties.In conclusion, the most effective digital twin strategies for batteries lie in hybrid models that combine the rigor of physics-based modeling with the flexibility of data-driven methods. These fast, interpretable, and scalable models are essential for advancing energy storage technologies and enabling real-time control and optimization in diverse applications.