With the rapid development of the power infrastructures and the increase in the number of electric vehicles (EVs), vehicle-to-grid (V2G) technologies have attracted great interest in both academia and industry as an energy management technology in 5G smart grid. Considering the inherently high mobility and low reliability of EVs, it is a great challenge for the smart grid to provide on-demand services for EVs. Therefore, we propose a novel smart grid architecture based on network slicing and edge computing technologies for the 5G smart grid. Under this architecture, the bidirectional traffic information between smart grids and EVs is collected to improve the EV charging experience and decrease the cost of energy service providers. In addition, the accurate prediction of EV charging behavior is also a challenge for V2G systems to improve the scheduling efficiency of EVs. Thus, we propose an EV charging behavior prediction scheme based on the hybrid artificial intelligence to identify targeted EVs and predict their charging behavior in this paper. Simulation results show that the proposed prediction scheme outperforms several state-of-the-art EV charging behavior prediction methods in terms of prediction accuracy and scheduling efficiency.

随着电力基础设施的快速发展和电动汽车（EV）数量的增加，车对网（V2G）技术作为5G智能电网中的能源管理技术已引起了学术界和行业的极大兴趣。考虑到电动汽车固有的高移动性和低可靠性，为智能电网提供电动汽车的按需服务是一个巨大的挑战。因此，我们针对5G智能电网提出了一种基于网络切片和边缘计算技术的新型智能电网架构。在这种架构下，智能电网和电动汽车之间的双向交通信息将被收集，以改善电动汽车的充电体验并降低能源服务提供商的成本。此外，EV充电行为的准确预测也是V2G系统提高EV调度效率的挑战。因此，本文提出了一种基于混合人工智能的电动汽车充电行为预测方案，以识别目标电动汽车并预测其充电行为。仿真结果表明，所提出的预测方案在预测精度和调度效率方面优于几种最新的电动汽车充电行为预测方法。