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August 23, 2025Open Access

Relevance of classical models/approaches for Battery Digital Twins

Key Points

Hybrid models improve battery performance by merging data-driven and physics-based approaches, enhancing efficiency.
Data-driven models are adaptable but struggle with data dependency and interpretability, noting their limitations in real-world applications.
Physics-based models, such as the Single Particle Model and Pseudo-2D models, provide high accuracy and require less operational data for implementation.
The study discusses various modeling techniques and their trade-offs in accuracy and computational efficiency for battery systems.

Abstract

Digital twins are virtual replicas of physical systems updated in real-time and are 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 are gaining popularity and offer wide ranging adaptability, they face limitations due to dependency on data, poor interpretability, and generalization. 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 weak form formulations are presented, showing the trade-off in accuracy and computational efficiency. Advanced modeling approaches for phase-separating chemistries like LFP are explored using phase-field methods as well as conversion chemistries like LiS using cell centered finite different techniques. 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.

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