Sustainable energy management in electric vehicle secure monitoring and blockchain machine learning model

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. The experimental study is done for several network characteristics in terms of scalability, QoS, data integrity, throughput, and endto-end latency. When compared to baseline control methods, experiments using real-world data demonstrate that the suggested algorithms can dramatically lower operational costs and peak power usage.