Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles

Transportation Network
Transportation Vehicles

Release Date:
Project Count:

Program Description:

Recent rapid advances in driver assistance technologies and the deployment of vehicles with increased levels of connectivity and automation have created multiple opportunities to improve the efficiency of future vehicle fleets beyond in new ways. The projects that make up ARPA-E's NEXTCAR Program, short for "NEXT-Generation Energy Technologies for Connected and Automated On-Road Vehicles," are enabling technologies that use connectivity and automation to co-optimize vehicle dynamic controls and powertrain operation, thereby reducing energy consumption of the vehicle. Vehicle dynamic and powertrain control technologies, implemented on a single vehicle basis, across a cohort of cooperating vehicles, or across the entire vehicle fleet, could significantly improve individual vehicle and, ultimately, fleet energy efficiency.

Innovation Need:

While a large portion of future vehicle energy efficiency improvements (driven by federal fuel economy standards) is expected to be achieved through a mix of well-established technologies, there is a significant new opportunity to leverage the advances of Connected and Automated Vehicle (CAV) technologies to further improve the energy efficiency of individual vehicles. By using onboard sensing and external connectivity such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X), NEXTCAR projects will allow a vehicle to “know” with some certainty its future operating environment. This will enable better coordination of vehicle-level and powertrain-level actions including acceleration, deceleration, grade climbing, efficient engine operation, efficient transmission operation and regenerative braking and battery state-of-charge management in the case of Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV). Whereas current powertrain control is reactive, with drivers and vehicle systems responding to events after they happen, powertrain control technologies developed under the NEXTCAR Program will demonstrate the potential to be predictive and anticipatory.

To date, developments in CAV have largely prioritized increasing safety and collision avoidance (in the case of V2V) and navigation and infotainment (for V2X). The ARPA-E NEXTCAR Program is among the first to consider energy in CAV applications, coordinating vehicle dynamic controls and powertrain operation to maximize vehicle efficiency under real-world driving conditions. The NEXTCAR Program emphasizes applications for vehicles that are not yet fully automated (NHTSA/SAE levels 0-3), or not yet capable of operating without a human present to intervene in certain situations.

Potential Impact:

If successful, developments from NEXTCAR projects will enable at least an additional 20% reduction in energy consumption of future connected and automated vehicles.


NEXTCAR program innovations could lead to a dramatically more efficient domestic vehicle fleet, lessening U.S. dependence on imported oil.


Greater efficiency in transportation can help reduce sector emissions, helping to improve urban air quality and decreasing the sector’s carbon footprint.


NEXTCAR program innovations would further solidify the United States’ status as a global leader in CAV technology, while a more efficient vehicle fleet would reduce energy cost per mile driven and bolster economic competitiveness.


Program Director:
Dr. Marina Sofos;Dr. Priyanka Bakaya;Dr. Christopher Atkinson
Press and General Inquiries Email:

Project Listing

• General Motors (GM) - InfoRich VD&PT Controls
• Michigan Technological University (MTU) - Hybrid Electric Vehicle Platooning Control
• Pacific Northwest National Laboratory (PNNL) - Autonomous Intelligent Assistant (AutonomIA): Resilient and Energy-Efficient City-wide Transportation Operations
• Pennsylvania State University (Penn State) - Fuel Efficiency through Co-Optimization
• Purdue University - Connected and Automated Class 8 Trucks
• Southwest Research Institute (SwRI) - Vehicle Model Predictive Control
• The Ohio State University - Engine Cylinder Optimization in Connected Vehicles
• University of California, Berkeley (UC Berkeley) - Predictive Data-Driven Automotive Control
• University of California, Riverside (UC Riverside) - Efficient Plug-In Hybrid Electric Buses
• University of Delaware (UD) - Optimized Vehicles through Connectivity
• University of Michigan - Integrated Vehicle Power & Thermal Management
• University of Minnesota (UMN) - Optimized Delivery Vehicles