# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 1 Introduction  
Modern power grids need to be smarter to provide a sustainable and affordable power supply in the context of ever-increasing consumer demand. The smart grid (SG) is a modern version of the traditional power network that is emerging as monitoring technology advances and power use continues to grow [1]. This network functionally includes control centers, transmission networks, and substations. The SG is a cost-effective distributed power line condition monitoring system that allows for the two-way flow of power and information between the customers and providers of an energy service, which improves the efficiency and stability of power delivery [2]. By using this two-way communication, the advanced metering infrastructure (AMI) makes the energy service not only reliable and secure, but also cost-effective throughout the system [3]. Technically, the smart grid is a modern solution that adopts a unique digital platform to provide innovative sensing [4], signal processing, communication, and computing capabilities [5]. Together with advanced metering and the automation of distribution, the creation of the technical foundation for a smart grid that combines power communication and computer control can lead to a significant improvement in reliability and grid capacity. Furthermore, smart grids track every data point in the system of the energy distributioncycle, which makes it possible to monitor and control the energy service in real time, including pricing, production, and consumption. Consequently, the energy service providers are able to achieve efficient load balancing and a reliable transfer of energy. As a result, smart grids provide better autonomous control measures and improved efficiency through innovative product type solutions. Along with the development of smart grids, the proportion of electric vehicles (EVs) is expected to increase due to climate concerns, rising crude oil prices, technological advances, and advances in the automatic control of EVs [6]. International Energy has a plan to have more than 20 million EVs by 2020. As EVs consistently need to be recharged, they require a plan for effective and intentional use of energy otherwise, a large number of EVs can cause an overload [7]. EVs and their peripheral products constitute a large disperse network of data-collecting elements, promoting the development of the Internet of Things (IoT) [8]. EVs can also promote urban development through the integration of renewable energy and comparatively low emissions. There are two modes of EV in the power grid: Consumers and producers. In this context, consumer means grid-to-vehicle (G2V) and the producer means vehicle-to-grid (V2G) [9]. In the consumer mode (G2V), the electric vehicle can be charged. In recent years, various electric vehicle charging scheduling schemes and pricing strategies have been proposed to reduce charging operation costs and improve grid reliability. A centralized hybrid game strategy for charging and discharging has been proposed in a V2G network [10], so as to eliminate the need for a centralized control system, developing a distributed charging strategy for plug-in hybrid vehicles. In addition to charging scheduling control [11], more and more research has been conducted on designing appropriate demand response mechanisms to improve overall system efficiency [12]. Dynamic pricing decision mechanisms based on charging stations and electric vehicles have been considered [13]. However, most existing studies assume a certain understanding of the arrival of future electric vehicles and electricity prices [14]. Some researchers have proposed that the Markov Decision Process (MDP) is based on the premise that the distribution of future events is known [15]. However, the assumed distribution and actual time distribution often do not match, resulting in inefficient pricing and scheduling strategies in actual scenarios. In recent years, machine learning has become one of the most active technologies in many research fields.