# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 3 Digital Twin Technologies for Electromobility
### Digital Twin and 5G Networks  
The 5G network supports a wide range of applications in different industries. The enhancement of the 5G network communication has a significant impact on Industry 4.0. Such industries are smart cities, military applications, healthcare systems, and transportation using IoT. The 5G network makes rapid changes in wireless communications and improves the wireless network performance by increasing capacity, improving reliability, lowering latency, and increasing the network speed [35]. The previous cellular technologies depend on fixed infrastructure, while 5G networks significantly enhance the use of small cell sites and mobile cell sites, increasing network access in congested areas. The 5G network has various applications and dynamically changes latency, bandwidth, and reliability requirements. These requirements have a high impact on the deployment of the 5G network in EVs.  
The 5G network is largely used in EVs for communication between the EV components. The rapid transformation and fusion between industry and communications systems have led to significant highway renovations, especially in self-driving. This development has affected many applications, such as the roll-out of 5G networks, the Internet of Vehicles, and the adoption of cellular vehicle-to-everything (C-V2X) connectivity. As a result, when 5G is connected, vehicles exchange traffic data, highway data, traffic signals information, roundabout information, etc., without human interference [36]. The 5G network-connected vehicles will generate a huge amount of data and have more autonomous functions. The 5G network integrated with DT can address key variables, such as capacity, reliability, mobility, latency, and security. Recently, a prediction method for 5G-enabled IoV in real-time traffic using the DT concept was introduced in [37]. [PERSON] et al. worked in IoV solutions and introduced a DT-assisted real-time traffic data prediction model using 5G communication. As a result of this work, the proposed model proved to optimize the scheduling of traffic resources and mitigate possible traffic jams at peak times. The authors believe that the proposed method can be more accurate than others by analyzing the traffic flow and velocity data measured by IoV sensors and transmitted over 5G communications.  
[PERSON] et al. [38] proposed a combined approach consisting of DT, reinforcement learning, and expert knowledge for the self-optimization of current 5G network performances, and they described potential application scenarios for 6G to solve the problem of end-to-end delay and reduce the processing time at the local servers in many emerging critical applications. However, [PERSON] et al. [39] adopted a digital twin framework of the current network. The proposed framework based on the DL algorithm achieved lower energy consumption with minimal computing complexity. [PERSON] et al. [40] proposed an innovative DT framework as an expandable approach for data-oriented modeling and the real-time simulation of extensive systems on 5G-supported IoT networks. The DT framework employs a tiered architecture for decentralized deployment on cloud computing platforms. Additionally, it facilitates the application of AI/ML engines for event identification and prediction models. It can be concluded that the use of 5G in combination wit hDT technology is essential in real-time scenarios, such as the IoV framework in which the safety and efficiency of the vehicle fleet traveling depends on the velocity and quality of the exchanged data.