An AI-enabled federated blockchain framework for adaptive energy coordination in smart electric mobility networks

The high rate of electric vehicles (EVs) development has motivated the issues of peak load congestion, data privacy, scalability, and secure energy coordination in smart electric mobility networks. The traditional centralized EV charging management systems have weaknesses of privacy leakage, single point failure, lack of real time flexibility and lack of trust in the transaction. This paper proposes a Privacy-preserving Edge -Trust -Adaptive Learning Framework (PETAL-Grid), an AI-based federation blockchain model to support adaptive and privacy-preserving energy coordination. The key goal of this study is to attain scalable, real-time and secure EV charging coordination through the integration of federated artificial intelligence, edge-based demand intelligence and blockchain enabled trust management. The proposed framework allows joint demand learning without the need to exchange raw data, real-time adaptive charging based on edge intelligence, and transparent and tamper-proof energy transactions based on smart contracts. The PETAL-Grid workflow comprises of local data collection, edge-based demand forecasting, federated model aggregation, adaptive load coordination, and blockchain-based transaction validation. The results of the simulation show that PETAL-Grid can attain 18% peak load reduction, 17% efficiency of energy utilization, and 98-99% transaction security, which are better than the centralized and the baseline models. The results validate that PETAL-Grid is a scalable, reliable and dependable solution to sustainable smart electric mobility networks.