Department of Energy Announces 10 New Projects to Improve Connected and Automated Vehicle Efficiency

ARPA-E Awards $32 Million to Use Connected Technologies for Vehicle Energy Savings

WASHINGTON — The Energy Department’s Advanced Research Projects Agency-Energy (ARPA-E) today announced up to $32 million in funding for 10 innovative projects as part of its newest program: Next-Generation Energy Technologies for Connected and Autonomous On-Road Vehicles (NEXTCAR). With a goal of reducing individual vehicle energy usage by 20 percent, NEXTCAR projects will take advantage of the increasingly complex and connected systems in today’s—and tomorrow’s—cars and trucks to drastically improve their energy efficiency.

Connected and automated vehicle (CAV) technology utilizes on-board or cloud-based sensors, data and computational capabilities to help a vehicle better process and react to its surrounding environment. This knowledge could include the location of stop signs and intersections, the actions of nearby vehicles, the location of congested areas, and much more. Currently, CAV technologies predominantly improve upon vehicle safety and add driving convenience. NEXTCAR projects will leverage these rapidly evolving technologies to greatly reduce vehicle energy use.

“Today cars and trucks are increasingly being outfitted with new technology that provides information about the vehicle’s environment, mostly to make them safer and to help drivers with basic tasks,” said ARPA-E Director Dr. Ellen D. Williams. “As our vehicles become creators and consumers of more and more data, we have a transformative opportunity to put that new information to the additional use of saving energy in our road transportation system.”

By “co-optimizing” the interactions between vehicle dynamic controls, like accelerator and braking input, and powertrain controls that manage engines, motors and transmissions, NEXTCAR technologies offer efficiency-boosting solutions like smarter cruise control and vehicle speed harmonization, or energy-saving options for approaching and departing from traffic signals. By integrating these systems with data from emerging CAV technologies, vehicles will be able to predict future driving conditions and events like changing road grade or the interactions with other vehicles merging from multiple intersections.

If successful, NEXTCAR technologies will improve the energy efficiency of future connected and automated vehicles by at least 20 percent beyond other planned vehicle efficiency technologies. As with all its programs, ARPA-E established this open-ended goal to encourage applicants to explore every available option, enabling a wide diversity of design approaches, technologies, and projects.

Further details on the NEXTCAR program can be found here. Details on all 10 of the NEXTCAR projects may be found here.

Examples of selected NEXTCAR projects:

University of Minnesota – Minneapolis, MN
Cloud Connected Delivery Vehicles: Boosting Energy Efficiency Using Physics-Aware Spatiotemporal Data Analytics and Real-Time Powertrain Control – $1,400,000

The University of Minnesota team and its partners seek to improve the energy efficiency of medium-duty delivery vehicles through real-time powertrain optimization using two-way vehicle-to-cloud (V2C) connectivity. Large delivery fleet operators already use extensive data analytics to assign routes for minimizing energy consumption. The project team will further improve the energy efficiency of their series hybrid-electric vehicle by optimizing battery state of charge and engine operating strategy in coordination with intelligent eco-routing. Using cloud connectivity, the vehicle will periodically download the most-efficient powertrain calibrations based on external data like traffic and weather collected while the vehicle is en route.

Purdue University – West Lafayette, IN
Enabling High-Efficiency Operation through Next-Generation Control Systems Development for Connected and Automated Class 8 Trucks – $5,000,000

Purdue University, together with its partners, has a multi-pronged approach for the implementation of their heavy-duty diesel truck project, focusing on concepts including: transmission and engine optimization; more efficient maintenance of exhaust after-treatment systems using look-ahead information; cloud-based remote engine and transmission recalibration; cloud-based engine and transmission control; and efficient truck platooning. The most promising strategies will be evaluated and refined using a phased approach relying on a combination of simulations, development and real-world testing.