## 8 Conclusions  
The basic objective of this study was to compare regression- and simulation-based energy consumption estimation. With this in mind, we constructed a simulation model to accurately determine the electric consumption of a BEV with a stable accuracy of less than 4% error, regardless of almost all conditions encountered in urban traffic. The parameters of the simulation models were defined based on the available real physical data of the vehicle--the models were not subjected to any traditional model training. However, in order to validate the model, the simulation results were compared with the real measurement data to arrive at robust research conclusions. It can be concluded that in certain operational design domains, a simulation model using technical specification-based data can give better results than regression models. The results demonstrate that the simulation model developed has achieved the desired objective. The vehicle consumption for all three urban trips was determined with an error of less than 4%. In contrast to the regression models, the results obtained are much more consistent, with smaller differences between the results. It is noteworthy that in certain instances, the consumption determined during the WLTP cycle provides a more accurate value than that obtained though regression models. However, the simulation still shows significantly more accurate results. In these cases, the disadvantage of fast but imprecise solutions comes into play. It is acknowledged that the computational demands of these models are not directly comparable to those of simulation. Consequently, in instances where the computational resources are limited, the utilization of regression models is unavoidable. The discrepancy between the actual and calculated results in simulation model development can be further reduced by the application of additional refinements to the models. The findings of the research indicate that the simulation of the electrical subsystems can yield precise results. It is recommended that future research includes the simulation of additional consumers, such as the entertainment system, audio system and electric brake assist. On the other hand, the simulation results indicate that the consumption of the powertrain and subsystems is a realistic representation of the current situation. The current results were obtained by testing a standard vehicle equipped with the relevant features. However, in order to obtain more accurate and detailed results, further measurements at subsystem level are required, which should be carried out using a specially prepared test vehicle. With this, based on the measurement data, it is proposed that additional subsystems be implemented in the model. It is anticipated that the simulation models will be more accurate. As an example, the infotainment system has the potential to significantly influence the overall system consumption, based on the preliminary findings. A subsystem model that simulates the essential components of the infotainment system and comfort electronics could offer valuable insights for future developments. It can be concluded that the failure of a vehicle system treated as a monolithic unit will be higher than if the vehicle is treated as an ensemble of subsystems and stimulated. In line with the above, if the necessary measurement data are available, our next research objective is to validate the subsystems of the simulation model. Improving the precision of the simulation under diverse operational scenarios will facilitate advancement in the research domain. The generation of accurate simulation data will enable the development of a new and innovative energy management system. The basic premise of this energy management approach is the ability to predict the consumption of different subsystems under varying conditions. This allows the electric vehicle's subsystems to be prioritized in the event of an emergency maneuver, depending on the power requirements of each electrical load under the conditions prevailing at the time.