Vehicle Model Predictive Control
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:
Southwest Research Institute (SwRI) will develop control strategies and technology to improve the energy efficiency of a 2017 Toyota Prius Prime plug-in hybrid electric vehicle through energy-conscious path planning and powertrain control. The team will modify the vehicle to take advantage of connected, autonomous vehicle information streams and develop systems that co-optimize the control of vehicle speed and engine power to minimize energy consumption, maintain safety, and deliver expected performance. Modern automobiles are designed to provide the maximum possible performance to the driver in terms of response time and acceleration. Because of this, manufacturers design engines for all possible scenarios a driver may encounter – often conflicting with efficiency needs. The SwRI team will approach this problem by augmenting the vehicle with the necessary hardware for V2V and V2I connectivity along-with leveraging the production Dynamic Radar Cruise Control (DRCC) feature of the vehicle. GPS will work with cellular data to optimize planned driving routes. Eco-approach and departure will work with traffic signals at intersections to optimize vehicle braking and acceleration for improving energy efficiency. Plug-in hybrid electric vehicles are capable of charge-depleting, and charge-sustaining modes, or combination of these two modes depending on how much the vehicle uses the electric battery or the internal combustion engine. The team will develop control algorithms that will use the new information streams to optimize the battery state of charge for both overall trip efficiency and for driving power. Vehicle testing will occur in two phases. First, it will provide the driver with information about the next plug-in opportunity and suggested route and speed profiles. Next, the project will take advantage of DRCC to fully automate longitudinal control including regulating speed and ensuring safe operation by maintaining adequate spacing between vehicles.
If successful, Southwest Research Institute’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.