*3.4. Test Eco-CACC-I Controllers in Microscopic Tra*ffi*c Simulation Software*

The Eco-CACC-I controllers for BEVs and ICEVs were implemented in the microscopic traffic simulation software INTEGRATION to evaluate their performance. The INTEGRATION software is a trip-based microscopic traffic assignment, simulation, and optimization model that has the capability of modeling networks of up to 3,000,000 vehicle departures. A more-detailed description of INTEGRATION is provided in the literature [27,28].

A simulated traffic network, composed of three signalized intersections, as shown in Figure 6, was used in this test. The major road has a free-flow speed of 40 mph, a speed at a capacity of 30 mph, a saturation flow rate of 1600 veh/h/lane, and a jam density of 160 veh/km/lane. The total length of the main direction roadway is 4000 m. The three traffic signals (1000 m apart) have the same signal timing plan with a 60-s cycle length and 42-s phase length for the main street with 5-s intergreen time (yellow and all-red time indication). The signal offsets are 0 s for all traffic signals. The traffic volume in the main direction is set to be 400 veh/h/lane.

Given that the energy optimum solutions for ICEVs and BEVs are very different for the downhill direction, here we set a −3% grade for the main direction road. We modeled a 2015 Nissan Leaf and a 2015 Honda Fit to represent BEVs and ICEVs in the simulation. Four scenarios, described below, were used to compare the vehicle trajectories.


**Figure 6.** A simulated traffic network with three signalized intersections.

The vehicle trajectories near the first signalized intersection in the four scenarios are presented in Figure 7. Figure 7a presents the speed trajectories of the uninformed drive for ICEVs. We can clearly see that a total of nine vehicles came to a full stop upstream of the first intersection (as demonstrated by the horizontal trajectory lines). Figure 7b presents the speed trajectories of an uninformed drive for BEVs, which were very similar to the trajectories in (a) with nine fully stopped vehicles before the intersection. Figure 7c presents the speed trajectories optimized by ICEV Eco-CACC-I. The findings in Section 3.2 and Table 1 show that an ICEV equipped with an Eco-CACC-I controller quickly reduced speed and then cruised at a constant speed to approach the intersection during red signal indication when traveling in the downhill direction. The INTEGRATION simulation results in scenario 3 were demonstrated to be consistent with our previous findings, and the vehicles produced very smooth trajectories without having to come to a full stop, as shown in Figure 7c. In addition, the findings in Section 3.1 and Table 1 showed that the proposed BEV Eco-CACC-I controller would suggest that the vehicle decelerate mildly to maximize the deceleration time when traversing a signalized intersection on a downhill roadway, which was consistent with the simulation results in scenario 4, as shown in Figure 7d. The test results in the four scenarios proved that the microscopic traffic simulation with the ICEV and BEV Eco-CACC-I controller enabled in the INTEGRATION software produced consistent results to our findings in Table 1. In addition, the comparison of the simulation results in scenarios 2 and 4 showed that the BEV Eco-CACC-I controller produced average savings of 9.3% in energy consumption and 3.9% in vehicle delay.

**Figure 7.** Comparison vehicle speed trajectories by (**a**) uninformed drive for ICEVs; (**b**) uninformed drive for BEVs; (**c**) ICEV Eco-CACC-I; (**d**) BEV Eco-CACC-I.

## **4. Conclusions and Future Work**

A review of the literature shows that there are several issues in the BEV eco-driving strategies developed from existing studies, including a lack of realistic energy consumption models to accurately compute the instantaneous energy consumption when BEVs travel through signalized intersections, and the lack of a vehicle dynamics model to constrain vehicle acceleration maneuvers. In addition, although several previous studies developed eco-driving strategies for ICEVs and BEVs, there is no direct comparison to demonstrate the differences in the energy-optimal solutions for each. To address these issues, this study developed a BEV Eco-CACC-I controller. The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory was formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location and signal timing, defined by vehicle characteristics and SPaT data shared under the connected vehicle environment. The optimal speed trajectory was computed in real-time by the proposed BEV Eco-CACC-I controller so that a BEV could follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate the performances under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and ICEVs was conducted to investigate the impact of vehicle engine types on eco-driving solutions. The comparison results illustrate that previous studies, which only considered the optimization of acceleration/deceleration and ignored the specific vehicle energy model, may not correctly compute the energy-optimal eco-driving solution for different vehicle types. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor

with three signalized intersections demonstrated that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections, and that the BEV Eco-CACC-I controller produced average savings of 9.3% in energy consumption and 3.9% in vehicle delays.

Although the proposed controller was demonstrated to produce very positive energy and delay savings for BEVs from the simulation tests, currently the developed Eco-CACC-I controller can only optimize BEVs or ICEVs separately. In future work, an integrated optimization for different types of vehicles will be considered in developing optimum solutions for mixed traffic conditions. In addition, more simulation tests with various traffic volumes, market penetration rates, signal timings, etc., will be considered to test the proposed controller further.

**Author Contributions:** Conceptualization, H.C. and H.A.R.; methodology, H.C. and H.A.R.; software, H.C. and H.A.R.; writing, H.C. and H.A.R.; funding acquisition, H.A.R. and H.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was co-funded by the Department of Energy through the Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office, Energy Efficient Mobility Systems Program under award number DE-EE0008209 and the University Mobility Equity Center (UMEC).

**Conflicts of Interest:** The authors declare no conflict of interest.
