# A Framework for Communicating and Building a Digital Twin Model of the Electric Car
## 1 Introduction  
The automotive industry has changed significantly in the era of Industry 4.0. Many new technologies have been implemented into production processes and products [1, 2]. At the same time, electric vehicles (EV) are becoming more and more popular due to the reduction in fossil fuel combustion and greenhouse gas emissions [3]. Additionally, artificial intelligence (AI) is having some impact on the automotive industry, for example in autonomous vehicles [4]. As a result, the car becomes more and more complex. It may be almost impossible to stop this trend until new regulations are developed. Various environmental conditions affect the operation of the EV drive with more or less random damage to the engine with charged batteries. The objectives of minimizing electrical energy or maximizing crew safety are set. Problems can be solved using simulators, such as computer simulations (digital twins) where new conditions and EV configuration are implemented. The high fidelity and flexibility of simulators or computer simulations provide opportunities to reflect the unusual conditions and states of the EV. Maneuvers are practiced on developed simulators, computer simulations are carried out, and then new procedures can be passed on to drivers. The term digital twin (DT) is used in connection with Industry 4.0. [5]. It is worth mentioning the development of DT over the years and looking forward to the future [6]. This trend can be found not only in the automotive industry [7], but also in the railway [8]and aviation [9] industries; the digital twin originated in the latter and is related to the Apollo 13 mission [10; 11]. Starting from 1959, the National Aeronautics and Space Administration (NASA) required hundreds of hours of simulator training. NASA produced high-fidelity simulators with a large number of computers and many formulas. In the case of the Apollo Program, astronauts trained on 15 simulators for every aspect of the mission. The simulators provided an accurate approximation of a space flight, so various failure scenarios could be practiced. The term DT was first used by NASA [12]. It appeared in the draft of a roadmap. Previously, a similar concept was introduced by Dr. [PERSON] in 2002 and was called the \"Conceptual Ideal for PLM\" [13]. However, it was NASA that provided the definition of DT as well as the application goals. For NASA these were as follows:  
* Testing the vehicle before launch with various parameters. * Real flight reflection, comparisons, and updates. * Diagnostic tool for finding the causes of faults or damage. * Investigation of new modifications not planned at the design stage. The term digital twin first appeared in 2010, but events on Apollo 13 indicate that this was a digital twin in action. Modifications tested on simulators had a similar impact on the performance of the spacecraft. Without these options, it was much more difficult for the crew to learn how to operate the damaged ship. The main task of the DT was accomplished, namely contributing new data and knowledge. Results were obtained quickly enough to be transmitted in time if the spacecraft was at risk. The example shows that the digital twin approach resulted in positive feedback even in very demanding and stressful circumstances. Over the years, the number of publications containing the term \"Digital Twin\" has increased significantly. As mentioned in [14], 29 relevant papers were published in 2016, while in 2021 there were already 2997 of them. Similar results apply to DTs in the automotive industry [15]. To systemize information about digital twins, a maturity model is described. The method evaluates DT in 7 categories with 31 features, as in Table 1. It helps one to assess the current stage of an application's development and to find ways to further improve it [16].