| Project Detail |
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 Ohio State University will develop and demonstrate a transformational powertrain control technology that uses vehicle connectivity and automated driving capabilities to enhance the energy consumption of a light duty passenger vehicle up-fitted with a mild hybrid system. At the core of the proposed powertrain control technology, is the use of a novel cylinder deactivation strategy called Dynamic Skip Fire which makes instantaneous decisions about which engine cylinders are fired or skipped thus significantly improving vehicle energy efficiency. Connected and automated vehicle technologies will allow route-based optimization of driving. Route terrain information including road slope, curvature, and speed limits will be used to calculate an energy-optimal speed trajectory for the vehicle. Traffic condition information based on V2I communication (such as traffic lights) will be used to further optimize route selection and optimize the vehicle and powertrain control. The vehicle will interact with traffic lights using Dedicated Short Range Communications and will stop and start from intersections using an energy-optimal speed trajectory. The integrated radar/camera sensor and V2V connectivity will be used to determine the immediate traffic around the vehicle. Finally, machine learning algorithms will be used to make intelligent powertrain and vehicle optimization decisions in continuously changing and uncertain environments.
Potential Impact:
If successful, Ohio State’s project will enable at least an additional 20% reduction in energy consumption of future connected and automated vehicles. |
