## 3 Results  
Simulation results are gathered using the IPG Control add-on, which allows real-time visualization of the results by plotting any selected quantities in user-defined graphs. To easily follow the evolution of the model states and determine if the results are plausible, the IPG Control interface is configured as shown in Figure 9.  
Figure 8: Load distribution example for an electric bus. Figure 9: IPG Control Interface. The top graph in the Figure presents the evolution of available energy [kWh] and available charge (AoC) [Ah] of the high-voltage battery, while the center graph illustrates the battery's State-of-Charge (SoC) [%], and current [A]. The bottom graph shows the actual and average electrical energy consumption [kWh/100 km] and absolute electrical energy consumption [kWh] during a simulation scenario of 500 s, representing a sector run of the entire digitized route. In real-life scenarios, the placement of boarded passengers inside the vehicle is difficult to control, as each passenger has different preferences or needs that dictate their chosen seating or standing location during the trip. The combined weight of multiple people impacts the overall weight distribution of the vehicle. Its effect on the energy efficiency of the buses is studied using three different cases. In these scenarios, the front, middle, and rear sections of the buses had higher load concentrations, respectively. Besides the passenger load on the vehicles, all simulation parameters are kept the same: driving path, elevation profiles, speed limit of 20 km/h, standard driver behavior, no corner cutting. By centralizing and analyzing simulation results related to the conventional transport solution (i.e., two full-size buses used to cover the previously defined routes), the below described data is observed. As mentioned in Table 5, the average electrical energy consumption is observed to be higher in scenarios where the majority of passenger weight is distributed to the rear of the bus. It should be noted that on the rear axle is where the drive motors are also placed. Having the weight distributed evenly on the sides and the majority of it placed in the middle of the bus helps increase energy efficiency and keeps the SOC of the high voltage battery pack at a higher level during the simulation. Average energy consumption is at its lowest level in Table 6. Moving further from the drive axle, a front-biased weight distribution simulation shows improved results based on rear-biased cases but does not match the efficiency of centered passenger mass inside the electric bus. Table 7 shows the results for the majority of weight placed in the frontal passenger area. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Initial** & **Final** \\ \hline PT.BCU.BattHV.SOC [\%] & 85 & 42.55876932 \\ Average energy consumption [kWh/km] & & 1.60527 \\ Energy consumption per passenger [kWh/km] & & 0.02675 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Full-size bus simulation results (centered weight distribution). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Initial** & **Final** \\ \hline PT.BCU.BattHV.SOC [\%] & 85 & 42.32546397 \\ Average energy consumption [kWh/km] & & 1.60935 \\ Energy consumption per passenger [kWh/km] & & 0.02682 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Full-size bus simulation results (front-biased weight distribution). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Initial** & **Final** \\ \hline PT.BCU.BattHV.SOC [\%] & 85 & 42.55876932 \\ Average energy consumption [kWh/km] & & 1.60527 \\ Energy consumption per passenger [kWh/km] & & 0.02675 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Full-size bus simulation results (rear-biased weight distribution).