## 3 Results and Discussion  
A test route chosen for a simulation study consists of both highway and city driving on local roads in Columbus, Ohio. Figure 6 shows the route on a map along with traffic light locations and speed limits. Table 3 shows relevant route characteristics. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Optimized Values** \\ \hline \(a\) [m/s\({}^{2}\)] & 2.96 \\ \(b\) [m/s\({}^{2}\)] & 1.54 \\ \(c_{1}\)[-] & 3.75 \\ \(\delta\) [-] & 1.44 \\ \(\theta_{offset}\) [m/s\({}^{2}\)] & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calibrated EDM parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Trip Length** & **Max. Speed** & **Avg. Speed** & **Traffic Lights** \\ \hline
8.4 km & 30 m/s & 23.3 m/s & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Route characteristics. Beginning with the fixed speed limit along the route, traffic patterns typical of daily driving are randomly generated by choosing a random location along the route, a random speed which is less than or equal to the original speed limit, and a random length of speed limit variation. Along the selected route, there are nine traffic lights. A similar approach is used to randomly set the traffic light phase and timing. Examples of randomly generated speed limits over the chosen route are shown in Figure 7, where colored (green or red) vertical lines indicate the traffic light location and the phase the light is in. The size of each vertical colored segment indicates how long the phase lasts. Figure 6: Map of route. Figure 7: Selected route with random traffic and SPaT. The ability of the MPC to avoid red lights and the corresponding FE benefit from the EMS is shown first, followed by the FE distribution over several randomly generated traffic and SPaT patterns. Figure 8 shows the speed profiles for both DP-ECMS and the baseline for one trip. When the vehicle is approaching an intersection during its red phase, the baseline driver initiates the stop sequence in anticipation of the red light. Since it has no knowledge of the signal timing, there is no way to determine if the light will turn green before the vehicle reaches the intersection. DP-ECMS is able to use available look-ahead data to calculate the appropriate speed to arrive at the light on green and adjusts accordingly. For all simulations, the DSRC range is assumed to be 500 m based on studies found in the literature [11; 32; 33]. Figure 9 shows the travel time versus the distance traveled. DP-ECMS slows the vehicle down just enough to pass through the green window when possible, while the baseline will always initiate a stop if the light is red. The travel time is approximately equal for both methods. Figure 10 compares the fuel consumption for the two systems. DP-ECMS achieves a 7.1% reduction in fuel consumed compared to the baseline. The use of DP over the full route, assuming prior knowledge of the vehicle instantaneous speed for the entire trip, provides an ideal fuel consumption. When compared to this DP solution (0.53 kg total fuel consumed), the baseline consumes 13% more fuel than DP (0.59 kg total fuel) and the proposed method uses 5% more fuel (0.56 kg total fuel) than DP. It is also shown in Figure 11  
Figure 8: Speed profiles of DP-ECMS vs. baseline. Figure 9: Time vs. distance for DP-ECMS and baseline. that both strategies are approximately charge-sustaining, and the corresponding adapted EFs are shown in Figure 12. Although tailpipe emissions are not directly considered in the objective function, reducing the number of traffic light stops provides the added benefit of emissions reduction [34]. Therefore, it is expected that the proposed EMS will reduce emissions along with fuel consumption. A direct way of addressing tailpipe emissions is to incorporate the emissions in the cost function and after-treatment system dynamics in the dynamic constraints [35]. A Monte Carlo analysis is conducted with a sample size of 85, where the random variables are SPaT and average traffic speed.