# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
###### Abstract  
In the era of technological transformation, mobility and transportation systems are becoming more intelligent and greener. Thanks to powerful technologies and tools, electric and autonomous vehicles are spreading worldwide, substituting internal combustion engine vehicles and revolutionizing the way to drive. In this context, this paper is an extended version of the paper \"Digital Twin in Intelligent Transportation Systems: a Review published in 2020 7 th International Conference on Control, Decision and Information Technologies (CoDIT)\". The aim of this paper is to provide a comprehensive review of the literature from the last five years on the use of digital twin (DT) technology for Intelligent Transportation Systems (ITSs), focusing on electric and autonomous vehicles. In particular, with respect to the previous work, the focus has been expanded to include DT integration with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, artificial intelligence (AI), machine learning (ML), and 5G for ITS. Moreover, this paper presents a broad perspective on challenges in EV applications, including tracking, monitoring, battery and charge management, connectivity, security, and privacy. In addition, this paper discusses how DT can be used to effectively address the current issues in electric vehicle services, such as tracking, monitoring, battery and charge management, connectivity, security, and privacy. digital twin; intelligent transportation system; electrical vehicles; autonomous vehicles; 5G; IoT; Big Data analytics +
Footnote †: journal: Applied Sciences  
Improving the EV user's experience relies on three essential components: the physical entities in the real world, the virtual models, and the data driven by these models. Integrating these components requires a simulation framework to simulate large-scale traffic scenarios [3]. The distribution of charging points, EV volume, and all dynamic operations in the EV network should be managed effectively and safely. To achieve this goal, simulation platforms can simulate the components of the EV network and their interactions. The simulation of operations could help us understand the nature of the physical product in each stage and collect information about the product's characteristics, which can be helpful in the development process. Most simulation platforms support the digital twin (DT) concept, which can simulate the real-world entity in the industrial environment. The DT concept is known as a virtual replica of a real-world object that can provide the ability to study the development of physical objects in a digital situation/environment. DT was considered one of the world's ten most strategic innovations in 2019, providing autonomous objects (e.g., self-driving cars) and immersive technologies, such as virtual reality (VR), augmented reality (AR), and quantum computing [4]. The main idea of this technology is to replicate the physical object's behavior in a virtual environment that can produce the same output as the actual physical object. Adopting the DT concept can reinforce the development of the industry and the academic section. Digital data can improve an engineering system's intelligence concerning the analytical evaluation, extrapolative diagnosis, and performance optimization. The results of the analysis can be used to make the product or process run better in the physical environment [5]. Academically, the DT concept was introduced in 2002 by [PERSON] et al. [6] at a special summit on product life-cycle management at the University of Michigan Lurie Engineering Center. The first adoption of this technology was by [PERSON] al. [7], who presented a digital framework to reproduce the structural behavior of an aircraft. Indeed, DT technology is widely used in multiple industrial sectors to facilitate maintenance operations and predict failures, allowing machines to interact with each other and humans.