## 6 Results and Discussion
### Comparison with Other Methods  
As has already been outlined in the preceding section, there are a number of well-established regression models which can be used to address the problem we are trying to solve through simulation. The following section will provide a more detailed description of the aforementioned models. In the course of our research, we identified two regression models for estimating the consumption of electric vehicles that align with the conditions, i.e., the aforementioned shorter urban trips in the city under consideration. The first model is the regression model described by [PERSON] et al. [32]. This particular example was selected due to its foundation in a comprehensive database of diverse vehicular types, thereby enabling the presentation of universally applicable data in accordance with the specified inputs. The model is based on four principal inputs, which are the initial battery State-of-Charge (SOC) (kWh) as \(x_{1}\), the average ambient temperature (\({}^{\circ}\)C) as \(x_{2}\), the distance traveled (km) as \(x_{3}\) and the average speed of the vehicle during the trip (km/h) as \(x_{4}\). \[Y=0.626+0.002x_{1}-0.030x_{2}+0.169x_{3}-0.004x_{4} \tag{40}\] \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Route ID** & **Propulsion** & **Steering** & **HVAC** & **Lighting** & **Total** \\ \hline Trip 1 & 2064.9 W & 39.1774 W & 25.3956 W & 63.5182 W & 2193 W \\ Trip 2 & 2229.8 W & 32.4158 W & 32.3741 W & 63.7000 W & 2358 W \\ Trip 3 & 1949.8 W & 44.8327 W & 31.7507 W & 63.484 W & 2090 W \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation result. The approach of the second model by [PERSON], [PERSON] et al. is slightly different from the studies mentioned above [31]. The aim of this benchmark model was to develop a methodology for determining the consumption of electric vehicles by examining the impact of traffic control elements on a given route. The inputs to the model were the length of the route (km) as \(L_{route}\), the number of stops (n), the probability of stops (%) as \(n_{exroute}\), and the average speed (km/h) of a given route as \(E_{({\it{structate}})}\). \[NC_{route}=-1007.25+81.88*L_{route}+30.20*n_{exroute}+22*E(\sigma_{route}) \tag{41}\]  
Although the regression models provide a robust approximation, the accuracy of the results is highly variable, depending on the specific routine chosen. To ensure the reliability of the validation process, all relevant data were recorded during the test vehicle journeys. Before starting the journey, the initial SOC and ignition temperature of the battery were read from the vehicle's main computer. During the journey, GNSS data were recorded to determine the distance of the journey and speed of the vehicle. The data were subjected to systematic observation and measurement over time. The number of traffic control devices and type of traffic control devices that instructed the vehicle to stop, slow down or accelerate were noted. The data were then used as input to the regression models. The data extracted from the Global Positioning System (GPS) were then incorporated into the simulation, allowing the speed and direction changes of the route to be used in the simulation model. The following table provides a comparative analysis of the different methods. As can be seen in the tables above (and in Figure 14), in addition to the regression models presented, an estimation of energy consumption based on the WLTP standard was also carried out, but our expectations for a WLTP-based estimation were not too ambitious. Besides WLTP, we also considered the NEDC test procedure as an alternative methodology for comparison purposes. However, the WLTP specifies higher dynamic accelerations and higher maximum speeds, takes better account of the drag coefficient, and, most importantly from our point of view, it also takes into account the rolling resistance of the tire.