## 2 Digital Twin Technology for V2G  
### Digital Twin Architecture and Implementation Framework  
The implementation of the digital twin technology for electric vehicle systems requires a structured, multi-layered architecture that facilitates real-time interactions between physical and virtual components. Recent advances in distributed energy systems have highlighted the significant impact of artificial intelligence on the planning and operation of smart grids [33], making digital twin architectures essential for managing the complex interactions between EVs and the grid infrastructure. This architecture consists of four primary layers that work synergistically to enable comprehensive system monitoring and control. The **physical layer** encompasses the actual electric vehicle with its embedded sensors, control units, and communication interfaces. This layer continuously generates operational data, including battery state, energy consumption patterns, location information, charging status, and environmental conditions. The data collection framework must be robust enough to handle the dynamic nature of vehicle operations while maintaining data integrity across diverse operating conditions. The **communication and data acquisition layer** serves as the critical interface between physical and digital systems. This layer manages bidirectional data flow through various communication protocols, including cellular networks, WiFi, and dedicated short-range communications (DSRC). The implementation must ensure low-latency data transmission while maintaining security protocols to protect sensitive vehicle and user information. The **digital model layer** represents the core computational engine where the virtual replica of the physical vehicle resides. This layer processes incoming data streams, executes multi-physics simulations, and generates predictive insights based on validated mathematical models. The model layer must be capable of real-time parameter adjustment and adaptive learning to maintain accuracy across varying operational scenarios. The **application service layer** provides the interface for end-users and system operators, delivering actionable insights, control commands, and optimization recommendations. This layer integrates with broader energy management systems and smart grid infrastructure to enable coordinated V2G operations. The integration of electric vehicles into smart grids requires sophisticated algorithms for energy management between V2G and G2V operations [34], which are facilitated through this application layer architecture. The multi-physics electric vehicle model developed in this work serves as a fundamental building block within the digital model layer of the proposed digital twin architecture. Specifically, the validated AVL CRUISETM M model operates as a specialized component of the computational engine that can process real-time data streams from the physical layer and generates predictive insights for the application service layer. The EV model functions primarily as a tool for effective energy consumption evaluation of electric vehicles over real use profiles, providing the essential foundation for AI-driven energy management algorithms. The integration mechanism works as follows: (1) Real-time vehicle sensor data (battery voltage, current, temperature, GPS coordinates) are collected through the communication and data acquisition layer; (2) These data streams are fed into the digital model layer where our multi-physics model performs parameter adaptation and state estimation; (3) The model executes forward simulations to predict energy consumption, optimal charging strategies, and grid interaction opportunities; (4) Results are propagated to the application service layer where AI algorithms utilize these predictions for V2G optimization decisions. This tight integration ensures that the digital twin maintains synchronization with the physical vehicle while providing the computational fidelity necessary for sophisticated energy management algorithms.