## 6 Results and Discussion  
### Vehicle Measurement Data  
The following section outlines the key findings of the study and presents a comparison of the results with those of existing methods as presented in the literature. In order to substantiate the applicability of the model, real-world measurements were taken with the test vehicle. The GNSS data were collected with a 1 s sample time, which is deemed sufficient for the purposes of this phase of the research project, given the relatively low speeds and low dynamic environment. The experimental measurements were implemented in three distinct test routes. The measurements were taken in a variety of conditions, over different periods, with varying traffic and on diverse road types. The diversity of routes with different characteristics provides an opportunity to study deviations in driving behavior. Our objective during the analysis was to improve the representation of the dynamic behavior of the system. The objective was to examine urban conditions, with a specific emphasis on the most congested profile. However, a measurement under typical traffic conditions was also conducted to ensure a comprehensive dataset. It should also be emphasized that the routes selected for the measurements are representative of routes in a classic European city, which are not typically designed and developed to meet the needs of electric vehicles [72]. The initial route (Figure 11) demonstrated a notable degree of congestion throughout the duration of the measurement. The majority of this section is comprised of uninterrupted straight roads with only a few instances of bends. (The overall speed profile of the Trip 1 is shown in Figure 1). The total length of the section is 9.09 km, as determined from the recorded GNSS data. The average speed achieved during this route was 14.3 km/h, which was primarily attributable to the average ratio of congestion. The vehicle consumption on this particular section of the route was 25 kWh/100 km. The entire journey was completed in the morning. The external temperature was recorded at 13 \({}^{\circ}\)C. The overall difference in the SOC of the vehicle's battery between the start and end of the journey, as determined by the calculation, was 3%. The SOC data of a battery pack provided information regarding the available capacity in relation to the rated capacity. The SOC value can be expressed as a percentage, with 0% indicating that the pack is empty and 100% indicating that the pack is fully charged [73]. In order to gain insight into the impact of congestion on driving performance, we undertook a series of trials on the second route (Figure 12) during a period of peak traffic congestion in the city. Due to traffic conditions and the impact of the urban traffic management system, the measurement required a significant number of stops. The speed limit was only reached on a few occasions. The average speed was 3.4 km/h, which is relatively low. The overall speed profile of the Trip 2 is shown in Figure 2). This is partly due to the congestion on the road, which resulted in extended periods of waiting at traffic lights. The calculated average consumption by the car was 28 kWh/100 km. Upon arrival at our destination, we observed a 3% reduction in the battery charge level. The third section (Figure 13) was the shortest (8.32 km), passing through a densely populated area. On this section, the average speed was moderate and there were several small bends along the route. The average speed was 21 km/h, which is to be expected in a residential area. (The overall speed profile of the Trip 3 is shown in Figure 3). The vehicle consumed slightly more energy than on the first journey, with a consumption rate of 25.6 kWh/100 km. A decrease of 2% was observed in the battery SOC. A summary of the data recorded during the aforementioned measurements can be found in Table 3.  
Figure 11: Trip 1 route. Figure 12: Trip 2 route. Table 3 presents a comprehensive overview of the data collected during the validation process.