## 5 Conclusions
#### AI Algorithm Implementation Example  
To demonstrate the model's AI integration capabilities, consider a reinforcement learning algorithm for optimal charging scheduling. The digital twin model serves as the environment where an AI agent learns to optimize charging decisions based on real-time grid conditions, electricity pricing, and user mobility patterns. The agent receives state information including current SoC, grid load, energy prices, and predicted trip requirements from the validated multi-physics model. Through trial-and-error learning, the AI algorithm discovers optimal policies that minimize charging costs while ensuring vehicle availability. The 1.28% RMSE accuracy ensures the AI agent learns from reliable state representations, leading to robust charging strategies that can be deployed in real-world V2G systems. The validated model provides the necessary simulation fidelity for the AI algorithm to generalize effectively from training to actual deployment scenarios. This work significantly advances artificial intelligence applications in power systems by establishing the foundational modelling infrastructure required for intelligent V2G implementations. The validated model enables AI algorithms to optimize complex multi-objective problems including energy demand forecasting, intelligent charging scheduling, and automated grid balancing strategies. This research demonstrates how digital twin technology serves as a critical enabler of AI applications in decentralized energy systems, where electric vehicles function as distributed energy storage resources supporting grid resilience through AI-coordinated participation in ancillary services. Future research directions include implementing deep reinforcement learning algorithms for V2G optimization using the validated model as the training environment, developing federated learning approaches for privacy-preserving AI coordination across distributed vehicle fleets, and scaling the digital twin framework to accommodate large EV fleets with distributed AI processing capabilities. Advanced applications encompass integrating weather forecasting and renewable energy prediction models for enhanced AI-driven energy management and developing explainable AI frameworks for transparent V2G decision-making processes. This research contributes significantly to the evolution of intelligent power systems by providing validated tools for AI-driven optimization of EV-grid interactions. The demonstrated capability to accurately predict vehicle energy consumption enables more efficient AI-coordinated utilization of renewable energy resources while reducing the environmental impact of transportation systems. The successful integration of digital twin technology with AI-based optimization algorithms represents a significant advancement in smart grid systems, providing a robust foundation for widespread deployment of intelligent V2G systems that leverage artificial intelligence for optimal performance and reliability. Future work will extend our validated multi-physics EV model for V2G systems by focusing on optimizing computational efficiency for large-scale fleet deployment and real-time operations. Privacy issues will be considered, although not directly by the authors. We will also explore applications to advanced grid services, so to develop robust frameworks for seamless integration with smart grid infrastructure and energy markets and enhance the model's adaptability for diverse EV configurations and grid conditions. Conceptualization, M.C. ; methodology, M.C. ; software, G.D.P. ; validation, G.D.P. ; formal analysis, M.C. and G.D.P. ; investigation, G.D.P. ; resources, M.C. ; data curation, G.D.P. ; writing--original draft preparation, M.C. ; writing--review and editing, M.C. ; visualization, G.D.P. ; supervision, M.C. ; project administration, M.C. ; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript. Activities described within the paper are self-funded. Not applicable.