Modern drivers are skilled at anticipating and reacting to the behavior of nearby vehicles and the environment in order to travel safely. Nevertheless, all drivers operate with an information gap – a level of uncertainty that limits vehicle energy efficiency. For instance, safe driving demands that drivers leave appropriate space between vehicles and cautiously approach intersections, because one can never fully know the intentions of nearby vehicles or yet unseen traffic conditions. Closing this information gap can enable vehicles to operate in more energy efficient ways. The increased development of connected and automated vehicle systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles. Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) technologies will allow a vehicle to “know” its future operating environment with some degree of certainty, greatly narrowing previous information gaps. By providing the ability to predict driving conditions, these technologies could operate the vehicle powertrain (including the engine, transmission, and other components) more intelligently, generating significant vehicle energy savings.
Project Innovation + Advantages:
The University of Delaware (UD) will develop and implement a control technology aimed at maximizing the energy efficiency of a 2016 Audi A3 plug-in hybrid vehicle by more than 20% without reducing the vehicle's drivability, performance, emissions, and safety. The technology will use connectivity between vehicles and infrastructure to co-optimize vehicle dynamic and powertrain controls. It will compute optimal routing for desired destinations while bypassing bottlenecks, accidents, special events, and other conditions that affect traffic flow. The vehicle will optimize acceleration and braking events in coordination with the hybrid powertrain controller such that energy efficiency is maintained, even in areas of congestion. The control technology will consist of a vehicle dynamic (VD) controller, a powertrain (PT) controller, and a supervisory controller. The supervisory controller will (1) oversee the VD and PT controllers, (2) communicate the internal and external data appropriately, (3) compute the optimal routing for any desired destination, (4) determine the regions where electric driving will have a major impact and derive a desired battery state-of-charge trajectory, and (5) create a description of the upcoming road segment from the connected data and communicate it to the VD controller. The VD controller will optimize the acceleration/deceleration and speed profile of the vehicle, and thus torque demand. The PT controller will compute the optimal nominal operation ("setpoints") for the engine, motor, battery, and transmission corresponding to the optimal solution of the VD controller. By considering the vehicle as part of a large system of many vehicles that are wirelessly connected to each other and to infrastructure, the project aims to significantly increase vehicle energy efficiency.
If successful, UD’s project will enable at least an additional 20% reduction in energy consumption of future connected and automated vehicles.
These 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 improve urban air quality and decreasing the sector’s carbon footprint.
Innovations would further solidify the United States’ status as a global leader in connected and automated vehicle technology, while a more efficient vehicle fleet would reduce energy cost per mile driven and bolster economic competitiveness.