Development of a UDDS-Comparable Framework for the Assessment of Connected and Automated Vehicle Fuel Saving Techniques
Since the mid-seventies, the trend toward the development and adoption of fuelefficient light-duty vehicles has become well established. As part of this trend, many complex strategies for powertrain efficiency have been evaluated and successfully marketed in the form of electric and hybrid electric vehicles (EVs and HEVs), among others. More recently, interest in connected and automated vehicle (CAV) eco-driving strategies as a means to reduce fuel consumption has increased. Despite these trends, assessments of even the simplest eco-driving concepts against existing vehicle powertrain measurement techniques have been hampered by the lack of a unified comparative baseline, owing to the disparity between each technologys approach. On the one hand, powertrain engineering focuses on the efficient response to a controlled driving profile, while on the other hand, eco-driving focuses on modifying the driving profile itself as a means to efficiency. In an effort to resolve this disconnect, the authors present the development of a simplified methodology for ensuring that eco-driving scenarios are contextualized against traditional drive-cycle based text techniques, in this case, the Urban Dynamometer Driving Schedule (UDDS). To accomplish this, a simulated road network is developed such that normal, non-automated driving results in a good approximation to the UDDS. The simulation environment is then used to demonstrate the fuel savings possible by the application of a representative eco-driving technique in a way that maintains context with traditional drive-cycle based fuel saving technologies. Using this technique, researchers will be able to directly compare the fuel savings of CAV and established powertrain technologies.
Autonomous vehicles, dynamic eco-driving, fuel-saving technologies, hybrid vehicles
Fredette, Danielle; Pavlich, Craig; and Özguner, Ümit, "Development of a UDDS-Comparable Framework for the Assessment of Connected and Automated Vehicle Fuel Saving Techniques" (2015). Engineering and Computer Science Faculty Publications. 337.