Scholarlink

<p>Electric vehicles (EVs) are anticipated to play a crucial role in the future of transportation systems. The integration of the Internet of Vehicles (IoV) in soon-to-be smart cities is expected to tackle several traffic-related issues, like alleviating traffic congestion and reducing accidents on the roads. This manuscript presents an innovative traffic pricing system for managing electric vehicle charging within the IoV framework in a smart city. The proposed approach, named the CLO-DAGCN technique, combines clouded leopard optimization and dual attention graph convolutional network. The main goal is to reduce traffic congestion, maximize energy efficiency, and improve driving comfort. The CLO technique is used to improve the smart grid system’s overall security, profitability, and environmental sustainability. Furthermore, DAGCN is used to forecast energy supply and demand. The proposed method is implemented in MATLAB, and its effectiveness is evaluated in comparison with existing methods. According to the results, the CLO-DAGCN strategy works better than the existing methods, which include the recurrent neural network (RNN), artificial neural network (ANN) and long short-term memory (LSTM). Compared to the current expenses of 1.5$ for ANN, 2.1$ for LSTM, and 2.5$ for RNN, the anticipated cost of implementing the suggested approach is 1.1$.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Electric vehicle charging management in smart cities with internet of vehicles and renewable energy sources using CLO-DAGCN approach

  • 期刊论文

  • 发表日期：

    2025年8月4日

查看全文
  • P. Selvam,
  • C. Ponmani,
  • N. Sumangala Devi,
  • C. Rohmingtluanga

摘要

Electric vehicles (EVs) are anticipated to play a crucial role in the future of transportation systems. The integration of the Internet of Vehicles (IoV) in soon-to-be smart cities is expected to tackle several traffic-related issues, like alleviating traffic congestion and reducing accidents on the roads. This manuscript presents an innovative traffic pricing system for managing electric vehicle charging within the IoV framework in a smart city. The proposed approach, named the CLO-DAGCN technique, combines clouded leopard optimization and dual attention graph convolutional network. The main goal is to reduce traffic congestion, maximize energy efficiency, and improve driving comfort. The CLO technique is used to improve the smart grid system’s overall security, profitability, and environmental sustainability. Furthermore, DAGCN is used to forecast energy supply and demand. The proposed method is implemented in MATLAB, and its effectiveness is evaluated in comparison with existing methods. According to the results, the CLO-DAGCN strategy works better than the existing methods, which include the recurrent neural network (RNN), artificial neural network (ANN) and long short-term memory (LSTM). Compared to the current expenses of 1.5$ for ANN, 2.1$ for LSTM, and 2.5$ for RNN, the anticipated cost of implementing the suggested approach is 1.1$.