# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 4 Experimental Results and Discussion
### Scenario 2: Variable velocity of the preceding vehicle. In a cooperative car-following model, the preceding vehicle may adjust its velocity according to the road state and the signal state. The scenario of vehicles with varying velocities was designed and the extensive experiments were conducted. In order to test the car-following behaviors and fuel consumption, the preceding vehicle will run at a variable velocity from 15 to 32.5 m/s. From Figures 17-20, we can see that the velocity and acceleration of the following vehicle changed with the preceding vehicle under two car-following strategies. It was shown that the trends of these two vehicles were the same, but the decelerations of vehicle 2 and vehicle 3 were displayed at the beginning of the standard MPC strategy, which is shown in Figure 17. The minimum velocity of the standard MPC strategy was 13.7 m/s, and the maximum velocity was 34 m/s, while the minimum velocity with the improved MPC strategy was 15 m/s and the maximum velocity was 32.7 m/s. Therefore, the improved MPC strategy had superiority in velocity control. With the same state of the preceding vehicle, the accelerations of the following vehicle vibrated more frequently along with the change of velocity under the standard MPC strategy, which was unexpected to the control of the vehicle. Figure 16: The carbon dioxide emissions in Scenario 1. Figure 15: The fuel consumption in Scenario 1. The higher fuel conservation and lower carbon dioxide emissions can be produced by the improved MPC strategy by predicting the running state of the preceding vehicle and executing a smaller acceleration. As shown in Figure 20, although the velocity of the preceding vehicle changed frequently, the acceleration of the following vehicle changed relatively moderately, which indicates that the improved MPC strategy achieved relatively stable control of the following vehicle. Figure 19: The accelerations of the following vehicle under the standard MPC strategy in Scenario 2. Figure 17: The velocities of the following vehicle under the standard MPC strategy in Scenario 2. Figures 21 and 22 demonstrate that the movements of the following vehicles were consistent with the trend of the preceding vehicle under two car-following strategies. According to the CTH strategies, the spacing between the preceding vehicle and the following vehicles increased along with the velocity. There was not any intersection in these three lines in position, which indicates that there was no collision between any of the two vehicles. From Figures 19 and 20, we can see that the maximum value of acceleration of the following vehicles was 3.4 m/s\({}^{2}\) under the standard MPC strategy and 2 m/s\({}^{2}\) under the improved MPC strategy, which demonstrates that the improved MPC strategy can effectively avoid rapid acceleration. Figure 21: The positions of the following vehicle under the standard MPC strategy in Scenario 2. Figure 20: The accelerations of the following vehicle under the improved MPC strategy in Scenario 2. Figures 23 and 24 show the performances of fuel consumption and carbon dioxide emissions under two car-following strategies. It was evident that the vehicles consumed more fuel and emitted more carbon dioxide when accelerating, while the improved MPC strategy reduced some unnecessary acceleration processes and reduced fuel consumption and emissions. According to Figure 23, the total fuel consumption under the improved MPC was 0.37351 L at 80 s, which was about 6.77% less than that in the standard MPC strategy. As shown in Figure 24, the carbon dioxide emissions under the improved MPC strategy were reduced by 7.91% by comparing them with the standard MPC strategy, which proves the effectiveness of the improved MPC strategy. To evaluate the accuracy of the simulation results, we conducted 10 experiments in each of the two scenarios and recorded the total fuel consumption for 80 s, as shown in Table 6.  
Based on the data in Table 6, we could calculate the confidence intervals for the strategies in the two scenarios.