## 5 Case Study
### Discussion  
In this study, analytical models were constructed based on the driving data of an EAA-305 electric bus on route 355. As described in this section, the data from different vehicles and different routes were collected for model validation to evaluate the general applicability of the analytical models proposed in this study. The driving conditions of two routes, namely route 352 and route 355, were analyzed. Route 355, 11.2 km long, passes by schools and markets in the downtown area and has more flat stretches and traffic lights, resulting in a lower and more variable driving speed on this route. Route 352, 21.1 km long, stretches along the Dadu Plateau to the downtown area of Taichung City and has a gentle upward slope and fewer traffic lights, so the driving speed on this route is higher and more consistent. Driving data from buses, EAA-301 on route 355 and EAA-592 on route 352, were collected, both of which were electric vehicles of the same model as bus EAA-305 in this study. The data from the two vehicles were collected from August 2018 to the end of March 2019, which corresponded with that of EAA-305 and this study's analytical models were compared in two schemes, namely Scheme A and Scheme B as follows:  
**This study** EAA-305 electric bus on route 355;  
**Scheme A** A different vehicle on the same route, that is, electric bus EAA-301 on route 355;  
**Scheme B** A different vehicle on a different route, that is, electric bus EAA-592 on route 352. We input the data of the two schemes into the analytical models. The evaluation results of the classification indicators are presented in Table 9. We found that the scores for Scheme A were closer to those of the analytical models used in this study, whereas the F1 score for Scheme B differed significantly from that of the other analytical models applied. Therefore, we concluded that the energy consumption classification for Scheme B was not suitable. Figure 10 shows the comparative distribution of speed and energy consumption in Schemes A and B in this study. From Figure 10a, we observed that the vehicle speeds in this study and Scheme A ranged from 21 to 30 km/h, whereas the vehicle speeds in Scheme B ranged between 31 km/h and 40 km/h. According to the analysis of the vehicle speed and power consumption efficiency in Table 2, medium energy consumption in this study and Scheme A ranged between 0.06 kWh and 0.12 kWh, whereas in Scheme B, it was 0.10-0.16 kWh. We analyzed the analytical models' power consumption classification methods by considering the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Mileage (km)**} & \multicolumn{2}{c}{**Driving Power**} & \multirow{2}{*}{**Power Consumption**} \\  & & & **Consumption (kWh)** & \\ \hline
2018-08 & 3290 & 6062.9 & 0.54 \\
2018-09 & 3575 & 6128.8 & 0.58 \\
2018-10 & 4779 & 6643.3 & 0.72 \\
2018-11 & 3133 & 4322.5 & 0.72 \\
2018-12 & 4132 & 5795.4 & 0.71 \\
2019-01 & 4508 & 6280.6 & 0.72 \\
2019-02 & 3457 & 4931.6 & 0.70 \\
2019-03 & 4763 & 7075.8 & 0.67 \\ Monthly average & 3955 & 5903 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistics on the monthly mileage and power consumption in operating data. power consumption ranges mentioned above and the proportion of driving energy consumption data in Figure 10b. The analysis results showed that this study had the same energy consumption classification as Scheme A, and their data proportions were both approximately 20%; the medium energy consumption of Scheme B was changed to 0.1-0.2 kWh, and its data proportion was increased from 12.5% to 18.5%. Figure 11 shows the evaluation results of the factors which had feature weights higher than 0.1 that influenced energy consumption. The vehicle speed, motor speed, and total voltage were the factors influencing energy consumption in this study and Scheme A, whereas the motor speed and total current were the influencing factors in Scheme B.