Developing an Eco-Cooperative Adaptive Cruise Control System for Electric Vehicles (Collaborative Project)

Project Abstract

This study develops an Eco-Corporative Adaptive Cruise Control system (Eco-CACC) for battery electric vehicles (BEVs) in the vicinity of signalized intersections and investigates the network-level benefits of this system. The BEV Eco-CACC algorithms provide real-time energy-efficient speeds to connected automated EVs to optimize their travel through signalized intersections using Signal Phasing and Timing (SPaT) information received from traffic signal controllers and surrounding traffic information received from in-vehicle sensors. First, a basic BEV Eco-CACC algorithm was developed for a single intersection. After, an advanced algorithm called BEV Eco-CACC MS was developed with the consideration of impacts from queues and multiple intersections. The developed BEV Eco-CACC algorithms were implemented and tested using the INTEGRATION microscopic simulation software, considering different levels of market penetration rates, traffic conditions, signal timings, road grades, and vehicle types. The test results indicate that the energy-optimum solution for BEVs is different from that for internal combustion engine vehicles (ICEVs), thus demonstrating the need for vehicle-tailored optimum trajectories. The simulation tests demonstrate the BEV Eco-CACC MS produces up to 11% energy savings to pass multiple intersections. Lastly, the study conducts a stated choice experiment to unveil the inclination of drivers towards the Eco-CACC system and to calculate its potential market share. The results indicate that the Eco-CACC system can be very successful and that the overall attitude of individuals in favor of adopting of the system is capable of overturning the lack of private return on investment.

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Impacts and Outcomes

The proposed system can be implemented into the electric vehicles to reduce energy consumption to pass signalized intersections.

Universities and Sponsoring Organizations Involved

Virginia Tech

University of Maryland

U.S. Department of Transportation Office of the Secretary-Research

Principal Investigators

Hao Chen (VT) Email:
Hesham Rakha (VT) Email:
Javier Bas Vicente (UMD)
Cinzia Cirillo (UMD) Email:
José L. Zofío, Universidad Autónoma de Madrid, Email:

Funding Sources and Amounts

USDOT: $140,001 (Federal), Virginia Tech: $50,000 (Match), University of Maryland: $20,000 (Match)

Completion Date

March 2020


Eco driving, Eco-Cooperative Adaptive Cruise Control, battery electric vehicles, multiple signalized intersections, microscopic traffic simulation, market share.