# A Framework for Communicating and Building a Digital Twin Model of the Electric Car
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Topic** & **Description** \\ \hline Battery digital twin [35] & Based on available measurements from the LIB module, a DT framework was proposed in which various algorithms estimate the battery charge state. The digital twin of the battery is compared to the real-world module. \\ \hline Health monitoring and prognosis of electric vehicle motor [36] & Data, such as speed, time, housing, and winding temperature, are collected to train an artificial neural network (ANN). The electric motor is modelled in Simulink and connected to the ANN. The engine health can be monitored with real engine data stored in the cloud. \\ \hline eGolf battery digital twin [37] & Data from the car is sent to the cloud via the 4G mobile network. The DT of the battery estimates the state of charge and health condition. Battery status can be diagnosed by the end-user while driving. \\ \hline Car twin [38] & Automotive systems, such as the anti-lock braking system, immobilization control module, power control module, power steering control module, and others, are modelled in Simulink. Input signals are collected from the car via the controller area network, comparing car simulation results with real-world data collect while driving a car. \begin{table}
\begin{tabular}{c c} \hline \hline
**Topic** & **Description** \\ \hline Battery digital twin [35] & Based on available measurements from the LIB module, a DT framework was proposed in which various algorithms estimate the battery charge state. The digital twin of the battery is compared to the real-world module. \\ \hline Health monitoring and prognosis of electric vehicle motor [36] & Data, such as speed, time, housing, and winding temperature, are collected to train an artificial neural network (ANN). The electric motor is modelled in Simulink and connected to the ANN. The engine health can be monitored with real engine data stored in the cloud. \\ \hline eGolf battery digital twin [37] & Data from the car is sent to the cloud via the 4G mobile network. The DT of the battery estimates the state of charge and health condition. Battery status can be diagnosed by the end-user while driving. \\ \hline Car twin [38] & Automotive systems, such as the anti-lock braking system, immobilization control module, power control module, power steering control module, and others, are modelled in Simulink. Input signals are collected from the car via the controller area network, comparing car simulation results with real-world data collect while driving a car.  
\end{table} 
Table 3: Digital twin examples in an EV world. * Hybrid energy storage system--modelling and simulation of the battery-supercapacitors system for the e-Golf. Studies have shown that the system can reduce energy consumption by 2.95% per 100 km [44]. Other aspects of Industry 4.0 are big data processing and cloud computing. Models for integrating sensor data with CPS, with the need to periodically query the controlled sensor network and process the received data in real time, are elaborated. Car data can be extracted using on-board diagnostic parameter IDs (OBD-II PIDs). Data are sent to the cloud to feed the digital twins. The authors in [37] point to the limited data quality and maximum sampling frequency of 9 Hz. However, the authors of [38] point out the lack of security requirements in CAN standards. Processing data and sharing results in real time is a challenge for DT implementation. Analyses based on historical data must be synchronized with the electric car's operating conditions in real time to enhance the capabilities of the physical and virtual worlds. The list of supported parameters is defined by a vehicle manufacturer. For EVs, additional services may be available to measure voltage, current, state of charge, and maximum battery temperature. The performance of EVs is tested in various conditions. In [45], the EV performance is tested in Bogota at an altitude of 2600 m above the sea level, and in [46] the effect of different ambient temperatures on driving behavior is investigated.