## 1 Introduction
Furthermore, the intermittent and unpredictable nature of individual charging patterns, driven by diverse battery features, mobility needs, and user preferences, significantly complicates grid planning and operation [19]. To address these complexities, artificial intelligence techniques have emerged as transformative tools for enhancing power system efficiency and reliability [20]. AI-powered algorithms enable advanced predictive analytics, energy flow optimization, and intelligent management of distributed resources, making them particularly well-suited for V2G implementation [21]. Machine learning approaches can process vast datasets encompassing EV charging patterns, grid conditions, and renewable generation to optimize scheduling and dispatch decisions in real-time [22]. However, the effectiveness of these AI applications fundamentally depends on accurate, validated models that provide reliable energy consumption predictions of EVs over real use profiles, for both algorithm training and final robust operational deployment. When combined with AI algorithms, digital twin technology creates a powerful framework for V2G optimization: virtual replicas can simulate diverse scenarios, predict system behaviour under varying conditions, and refine control strategies before real-world implementation [23]. The capacity to test and validate algorithms in virtual environments reduces operational risks while enabling rapid prototyping of innovative energy management approaches [24]. The intricate nature of EV-grid interactions, therefore, demands comprehensive simulation tools capable of modelling multifaceted relationships among vehicles, charging infrastructure, and power systems [25]. Effective digital twin platforms must accurately represent vehicle behaviour patterns, charging dynamics, and grid response characteristics across various operational scenarios [26]. Such capabilities are essential for evaluating the technical and economic feasibility of integration strategies, optimizing infrastructure deployment, and developing robust control algorithms for large-scale implementation [27]. For electric vehicles specifically, multi-physics modelling approaches prove particularly crucial in capturing complex interactions between electrical, thermal, and mechanical systems [28]. These models account for battery electrochemical dynamics, thermal management systems, power electronics behaviour, and mechanical drivetrain characteristics to deliver accurate performance and energy consumption predictions [29]. Developing high-fidelity multi-physics models remains fundamental for creating reliable digital twins capable of supporting sophisticated AI-based optimization algorithms [30; 31]. This work developed and validated a high-fidelity multi-physics EV model, serving as a fundamental building block for digital twin-enabled Vehicle-to-Grid (V2G) systems. Utilizing AVL CRUISE(tm) M, this framework accurately captures complex EV electrical, thermal, and mechanical interactions. Experimentally validated with real urban driving data from Naples, Italy, the model demonstrates exceptional accuracy, proving its reliability for intelligent V2G applications and providing a basis for AI-driven energy management. Addressing the complexities of integrating EVs with power systems, computational scheduling methods have evolved to coordinate such systems [32]. Despite advancements in EV modelling and V2G, a notable gap persists in high-fidelity, multi-physics EV models rigorously validated with real-world data. Specifically, foundational 'computational engines' within comprehensive digital twin architectures for AI-driven energy management in dynamic V2G scenarios are lacking. Existing approaches often sacrifice fidelity for speed, limiting predictive accuracy. Our main contributions to filling these gaps are:  
* Development and comprehensive validation of a high-fidelity multi-physics EV model, accurately capturing powertrain, battery, and regenerative braking dynamics, against real-world driving data.