# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
###### Abstract
In particular, DTs are used in a wide range of applications, including transportation, manufacturing, medicine, business, education, and more. Integrated machine-driven, electrical, and computer software systems can be simulated in the virtual workspace through DT technology [8]. One important example of such systems involves EVs, where new technology is needed to continuously optimize vehicle performance. DT technology can be an innovative solution for EV optimization. Many features of an electric EV can be computerized to increase its efficiency, performance, and smartness. Integrating Big Data analytics, IoT, and artificial intelligence (AI) technologies with DT leads to new significance, prospects, and challenges. Furthermore, an intelligent DT model can only be created using advanced AI technologies applied to the data [9]. It can address significant challenges in manufacturing, such as improving process stability, fault diagnosis, reducing downtime, and optimizing logistics processes [10]. AI can further enhance DT technology by using analytical models to process raw data into valuable digital forms. In this context, machine learning (ML) algorithms and technologies are currently used in EVs. In particular, ML algorithms can be used effectively if combined with predictive testing tools and DT technology. The importance of DT is also proved in the security and monitoring systems. [PERSON] et al. [11] presented a DT-enabled anomaly detection system based on industry foundation classes for asset-monitoring solutions. The proposed framework was evaluated using a case study to control the Heating, ventilation, and air conditioning system, and the system efficiently contributes to monitoring building assets. There is a lack of literature reviews that explore the use of DT in Intelligent Transportation Systems (ITSs), especially for EVs and autonomous vehicles (AVs). Due to the rapid development of the smart mobility industry, there is an urgent need to study and analyze the challenges and issues that the new generation of transportation will produce and how these can be addressed [12]. In response to the growing adoption of DT technology in ITS, this survey investigates recent literature, particularly emphasizing the application of DT in electromobility and self-driving systems. By exploring the integration of emergingtechnologies, communication tools, and DT concepts, our findings reveal the potential of DT technology to address challenges, such as cost-effectiveness, reliability, visualization, charging time, and intrusion detection in electric and autonomous vehicle networks. Furthermore, this survey highlights the role of data analytics and machine learning techniques in securing the ITS network and improving overall efficiency. We searched multiple databases, including Scopus, Google Scholar, and Web of Science, to identify relevant studies published in the last five years. In addition, we considered additional databases and information sources, such as ResearchGate. The provided review was carried out systematically, as shown in Figure 1, considering specific domains within smart manufacturing and transportation in which DT technology is applied, in combination with IoT, ML, AI, and 5G technologies. First, we selected a set of papers from the publication databases by considering the following keywords: DT, ITS, EVs, Bigdata, 5G, IoT, and AVs. From such paper sets, we singled out a subset of articles on the basis of their relevance and quality. Then, the paper discusses the current issues in EV services, such as tracking, monitoring, battery management systems (BMSs), connectivity, privacy, and security, and how they can be addressed effectively through DT technology. We highlighted the next EV revolution, i.e., electric AVs, and discussed the importance of data analytics roles in applying the DT concept in such a context. In Figure 2, a geographical distribution map displays the dispersion of the research articles examined in this review paper.