AI-Integrated Digital Twin for Cloud-Connected Battery Management in Electric Vehicles

Electric vehicles (EVs) need proper battery management for both their operational abilities as well as safety and extended lifespan. The functionality of standard Battery Management Systems (BMS) remains restricted due to problems in real-time adaptation as well as data synchronization difficulties. It suggests implementing a cloud-based EV battery management system through an AI-integrated Digital Twin framework, which unites deep neural networks (DNN) and federated learning technologies and edge-cloud architecture. A comprehensive simulation analysis took place with SUMO alongside AWS IoT Core. The implemented model proved its ability to predict SOC values with $\mathbf{9 8. 7 \%}$ accuracy and forecast SOH with $97.2 \%$ accuracy, along with fault detection accuracy reaching $96.5 \%$. The cloud communication system reached reduced latency levels at 150 ms successively, and the efficiency of federated learning operation increased by $38 \%$. The proposed method delivered better performance results compared to potential approaches. Real-time monitoring and predictive maintenance demonstrate strong effectiveness because of the obtained results. The upcoming work will target dynamic multicell battery system reconfiguration while conducting tests of their performance in various driving contexts.