Abstract:

Electric vehicles (EVs) are seen as one of the most promising methods to combat climate change, primarily because they lessen reliance on fossil fuels and the pollutants that result from fuel combustion. This study suggests a unique approach to managing the energy consumption of electric vehicles while analysing security utilizing blockchain machine learning (ML) algorithms. In this case, an adaptive fuzzy-based cross hierarchical reinforcement Q learning model (FCHRQL) is used to regulate the energy consumption of electric vehicles. Then, blockchain transfer federated learning (BTFL) is used to monitor security. A number of network properties, including scalability, QoS, data integrity, throughput, and end-to-end latency, are experimentally studied. Experiments using real-world data show that the proposed algorithms may significantly reduce peak power consumption and operating expenses when compared to baseline control approaches.