## 3 Results and Discussion
As a next step, the influence of speed and acceleration thresholds on the classification accuracy were analyzed. In Figure 10, the confusion matrix for test route 1 during the evening session is shown. Increasing the threshold values led to more data being correctly classified. When threshold values were increased by 5%, 167 samples were classified as UHT and 77 as ULT, as shown in the confusion matrix. In this case, five samples were wrongly classified as SHT, three as HLT, and four as HHT. When threshold values decreased by 5%, 143 samples were classified as UHT and 66 as ULT. In this case, 35, 6 and 7 samples were wrongly classified as SHT, SLT and HLT, respectively. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Speed Sampling Interval** & **Acceleration Sampling Interval** & **Training Accuracy** & **Training Loss** & **Validation Accuracy** & **Validation Loss** \\ \hline Data collection timing: early morning & & & & \\ \hline
100 s & 100 s & 0.7750 & 0.9920 & 0.7566 & 0.8190 \\ \hline
10 s & 0.9777 & 0.0229 & 0.9622 & 0.0239 \\ \hline
5 s & 0.9745 & 0.0265 & 0.9961 & 0.0260 \\ \hline Data collection timing: evening & & & & \\ \hline
100 s & 100 s & 0.4580 & 0.8997 & 0.5632 & 0.8921 \\ \hline
10 s & 0.9777 & 0.0452 & 0.9665 & 0.1479 \\ \hline
5 s & 0.9710 & 0.0322 & 0.9691 & 0.1098 \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy and loss over 100 s speed samples for test route \(1\) with varying acceleration samples. Figure 9: Labels for test route 1 sampled every 100 s for (**a**) evening and (**b**) early morning. In Figure 11, the confusion matrix for test route 1 during the morning session is shown. Increasing the threshold values led to more data being correctly classified. When threshold values increased by 5%, 92 samples were classified as ULT and 52 as UHT. In this case, four samples were wrongly classified as SHT and one as HLT. When threshold values decreased by 5%, 79 and 65 samples were classified as ULT and UHT, respectively. In this case, four samples were wrongly classified as SHT. The relative importance of the four features, namely speed, positive and negative acceleration, and number of stops, was evaluated through F-score and p-value to measure the effectiveness with which each of these features separates the different classes. As shown in Table 6, for both routes, speed is the most determining feature for classification as its F score value is the highest. For test route 1, positive and negative acceleration do not play such vital roles as in test route 2. This is because this route has higher changes in acceleration and deceleration values. Moreover, the number of stops has a higher occurrence in test route 1 than test route 2, and hence a higher F score value in test route 1. The \(p\)-value indicates the relevancy of the feature for classification. Since p-values are less than 0.05 for all features, they are all relevant in the classification process. Figure 11: Confusion matrix for test route 1 during the morning with threshold values: (a) decreased by 5%; (b) increased by 5%. Figure 10: Confusion matrix for test route 1 during the evening with threshold values: (a) decreased by 5%; (b) increased by 5%. The energy management problem solved using SQP provides the reference speed values for optimized energy consumption of the EV. As shown in Figure 12, with the mixed speed profile, the classification using DPR provides information about the type of driving and traffic situation. EMS provides reference speed values corresponding to minimum energy consumption. The energy consumption of the vehicle and the battery state of charge (SOC) are shown in Figures 13 and 14, respectively. The energy is observed to be increasing over time and the battery SOC has been depleted to a minimum value over time.