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:
General Motors will lead a team to develop "InfoRich" vehicle technologies that will combine advances in vehicle dynamic and powertrain control technologies with recent vehicle connectivity and automation technologies. The result will be a light duty gasoline vehicle that demonstrates greater than 20% fuel consumption reduction over current production vehicles while meeting all safety and exhaust emissions standards. On-board sensors and connected data will provide the vehicle with additional information such as the status of a traffic signal before a vehicle reaches an intersection, as well as traffic, weather, and accident information. This preview information enables the vehicle (and the driver) not only to react to current road conditions but also to plan for expected future conditions more efficiently. A proposed supervisory vehicle dynamic and powertrain controller will incorporate all the information available through connectivity and on-board sensors into an upper-level optimizer that determines the most fuel-efficient and safest vehicle operation. The upper-level optimizer sends brake, steering, speed, and torque requests to the two lower-level controllers: the vehicle dynamics controller (i.e. steering, acceleration and braking) and powertrain (i.e. engine, transmission) controller. The lower-level controllers, in turn, optimize their individual requests and send out commands to control the vehicle and powertrain. Overall energy efficiency increases by forecasting stopping events as early as possible, smoothing and reducing heavy acceleration, harmonizing speed, and optimizing the vehicle when approaching hills. The project combines General Motors' advanced vehicle/powertrain controls with Carnegie Mellon University's expertise in autonomous vehicles. Extensive real-world driving data available from the National Renewable Energy Laboratory's Transportation Secure Data Center and on-road tests will be used to validate improvements in fuel efficiency and assess real-world impacts.
If successful, General Motors’ project will enable at least an additional 20% reduction in energy consumption of future connected and automated vehicles.
These project 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.