## 3 Numerical Modeling and Novel Technologies and Materials
### Drives and Electric Vehicles
in [224] presented an application based design method that focuses on implementation of the electrical machine design process for EV electric drive system requirements. As stated in the research, such an application based design methodology allows shifting the highest electrical machine efficiency regions to the real operating points of the target EV. As a result, a customized electrical machine design for a specific EV is presented. Usually, design optimization is quite often based on the application of an electric drive system; for example, Reference [228; 229] considered the driving cycles of an EV as a reference. The paper by [PERSON] et al. [228] dealt with a high-speed PM assisted synchronous reluctance motor design and its optimization according to U.S. standard driving cycles (city: Urban Dynamometer Driving Schedule (UDDS); highway: Highway Fuel Economy Driving Schedule (HWFET)) to evaluate the most representative operating points of the studied application. The main results of the research were the most efficient operation areas and electromechanical specifications of an electrical machine. In order to define optimization inputs, a geometrical parameter analysis of the permanent magnet synchronous machine was performed. [PERSON] et al. [229] considered geometrical defects that are caused by electrical machine manufacturing errors, ambient conditions (including traffic and weather), and possible differences in driving styles to obtain robustification. The object of the study was a permanent magnet synchronous machine, where the rotor's and permanent magnets' geometries varied. Three different scenarios were observed, and the optimal electrical machine configuration for each scenario was suggested; however, the optimization results were very similar to the initial configuration, as the nominally optimized configuration was never robust. Optimization may also be implemented in the case of autonomous EVs, such as [230], e.g., for distribution path optimization. In [231], to avoid the confidence that uncertain transport time data are assumed (beyond the probability distribution), the authors used a robust optimization method. An optimization framework for quickly finding routes for EVs was presented in [232] by [PERSON];the study was focused on total energy consumption during the minimum requested time, stating that the main routing problem in the current study was solved. The influence of EVs on the market affects all infrastructures, and robust optimization may be also applied to the operation and planning of power systems, for example during charging of EVs. [PERSON] et al. in [233] proposed a robust optimization based method for optimal charging/discharging of EVs considering the electricity price uncertainties. It can be concluded that the optimization of an electric drive system is slightly dependent on the robustness of a single component; to achieve a robust drive system, a robust design optimization should be done at the system level. Different levels of electric drive designs are shown in Figure 9. As can be seen from the figure, a number of different sub-level optimizations must be considered together with the electrical machine; for example, the efficiency maps of the gearbox and inverter and the parameters of the controller. The Digital Twin (DT) [234; 235], as a physical replica of a system, presents a new approach in modeling and optimization, as well as in control, maintenance, diagnostics, and many other services [236]. For maximum performance, DT needs to have a specific algorithm to be characterized in virtual assets. While the life-cycle of the electrical drive as a product lasts several decades [237], it is important for DT to be reliable during all of the exploitation period; therefore, special attention must be given to the applied software as well.