This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The research effort supported by this award is aimed at formulating algorithms that use preview information of terrain, traffic signal timing, and traffic flow for saving fuel and reducing emissions of modern vehicles with conventional or hybrid powertrains. Terrain and traffic information is now available through various providers and can be integrated into the vehicle navigation system or into its add-on accessories. However systematic methods for utilizing this rich and dynamic information do not exist. The focus of this proposal is on solutions that plan over time the best vehicle velocity profile (or battery utilization for hybrid vehicles) that reduces fuel consumption and emissions without increasing trip time. The problem will be formulated in a dynamic constrained optimization framework with partial future information. Standard numerical solution via dynamic programming is viable but requires full information of future events. To complement the partial future information, we create predictive traffic models that use streaming traffic data to forecast evolving traffic patterns down the road. Receding finite-horizon optimization will be also explored as a mean to tackle missing information and the computational cost of dynamic programming. This also helps finding appropriate preview lengths and preview quality that result in meaningful fuel savings and/or emission reduction. Via multi-vehicle simulations the impact a few equipped vehicles can have on the flow of mixed traffic will be investigated. These results will be verified experimentally in collaboration with BMW's Information Technology Research Center in South Carolina.
If successful, this research can transform the way we drive our vehicles. The proposed solutions enable fuel saving and reduction of emissions relying mostly on software and information and with minimal hardware investments. This impacts not only the high-tech vehicle of the future but the current fleet when equipped with add-on accessories. In one scenario, a downloadable code can suggest the eco-friendly speed to the driver based on future state of terrain, traffic, and traffic signal. Future vehicles can automatically switch to the eco-friendly speed when in adaptive cruise control mode. With increased penetration of such solutions, there can be dramatic reduction in CO2 emissions and total national fuel use with direct societal and economical impacts. The interdisciplinary nature of this research will build a new bridge between mechanical engineering, electrical engineering and computer science, traffic engineering, and information technology disciplines, and creates novel educational opportunities.
