# A Framework for Communicating and Building a Digital Twin Model of the Electric Car
## 5 Conclusions and Future Work  
The paper presents the DT concept of the electric car with ViL simulation for more economical turning and at higher speeds. Connecting the ViL simulation with the digital model is a novel approach. The research station uses physical parts, while the kinematics of the vehicle is modelled with the software. Using physical parts instead of modeled components is beneficial because it increases the model accuracy. The actual status of the application development allows for static and deterministic simulation in which past electric car races can be carried out. Components of the research station are the same as used in the case of the EV. Kinematics of the vehicle in a specific time period are modelled using historical data. Figure 10: Comparison of the travel trajectories on the Dunsfold track: blue line—telemetry GPS track; green line—theoretical track. Further research requires validation. It means more measurements and further data integration. To obtain better results, the research stand includes the telemetry system installed in the electric car. With the current data on the DC motor, the proposed control values may differ. To improve the final simulation performance, a torque sensor with a smaller range should be installed. Then, the measured torque value can be compared with the calculated one. More data are better for comparison results with the DC motor data sheet and historic and actual telemetry data. Using only the AC motor encoder for measuring the rotational speed is not sufficient to verify the correctness of the solution. To complete the model, validation of kinematics is required to measure the DC motor current on the research station. The impact of set values on the DC current is possible to test. Finally, both the rotational speed and the current values of the DC motor need to be compared with the historic data collected by the telemetry system during the race. The simulation results should obtain similar values for the time lap, rotational speed and current of the DC motor. The main challenge for the validation process will be to compare the simulation results between different racing tracks and find out whether there is a common pattern for this process. The biggest limitation of the validation is the quality and the quantity of historical data about a specific event and car. The digital twin approximates various events, but never reflects real-world scenarios one-to-one. Future steps include a machine learning algorithm for computing setpoint values of the AC motor for different previous races. After validation, optimization of battery use or DC motor operation in changing weather conditions will be tested. A very important step is to automate the flow of data from the EV to the research station in order to build a digital shadow. It will simplify the commissioning procedure on the research stand. A later step is to automate the flow of data from the research station to the race car. Such a system as DT will guarantee two-way data exchange in real time during a racing event. Additional cloud computing and optimization results from the research station will impact the physical object. It is the most complex and demanding concept, while offering the best results. Converting the digital model into a digital twin will require a more advanced system in both the race car and the research station. Data from the race car must be send to cloud via 4G/5G network. The biggest challenge now is delivering results on time. The results generated should have an impact on the racing event. The system integration further will complicate the application. Building a scalable solution for different vehicles moving in parallel will need more digital twin platforms. In relation to the automotive industry, it is also important to find a safe way to obtain data without leaking them. Of course, future work must not affect the safety of the end user. The digital twin model presented in the article was verified for light electric cars driving in energy shortage conditions.