Digital Library on Green Mobility


Sustainable Energy Management in Electric Vehicle Secure Monitoring and Blockchain Machine Learning Model

Publication Year: 2024

Author(s): Jin W, Li C, Zheng M Y


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.

Source of Publication: Computers and Electrical Engineering

Vol/Issue: 115: 109093

DOI No.: 10.1016/j.compeleceng.2024.109093

Country: China

Publisher/Organisation: Elsevier

Rights: Elsevier Ltd.


Theme: Sustainable transportation | Subtheme: Energy

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