Congratulations to Dr. Fei Miao for receiving 2 NSF awards titled: S&AS: FND: COLLAB: Adaptable Vehicular Sensing and Control for Fleet-Oriented Systems in Smart Cities and CPS: Small: Collaborative Research: Improving Efficiency of Electric Vehicle Fleets: A Data-Driven Control Framework for Heterogeneous Mobile CPS Project Period.
In smart cities of the future, how to sense, understand, and manage urban-scale vehicular systems, e.g., taxis, in an autonomous fashion (with little or no human intervention) is an essential topic to improve urban mobility efficiency, such as shorter waiting time for passengers, lower cruising mileage for drivers, and higher revenues for vehicular system operators. However, the current management strategies for vehicular systems are mainly based on individual-level data knowledge, ignoring rich information from a fleet perspective. In the Smart and Autonomous Systems Program project (S&AS), the research team design and implement a fleet-oriented management strategy for vehicular systems, which utilizes real-time data from sensors installed in all vehicles to improve the overall performance of the vehicular system.
As electric vehicle technologies become mature, they have been rapidly adopted in modern transportation systems, such as electric taxis, electric buses, electric trucks, and shared-personal rental electric vehicles, due to their environment-friendly nature. Since electric vehicles require frequent yet time-consuming recharges, their dispatching and charging activities have to be managed efficiently considering the high charging demand of large-scale electric vehicles and the limited charging infrastructure. Therefore, an efficient electric vehicle management framework has the potential to (i) reduce the traveling distance to a charging station, (ii) reduce the wait time for electric vehicles to charge, and (iii) balance the demand and supply for charging infrastructure. However, current management strategies for electric vehicles are mainly based on homogeneous electric vehicles, ignoring challenges and opportunities introduced by heterogeneous electric vehicles, for example, electric personal vehicles, electric taxis, and electric buses. In the Cyber-physical System Program project (CPS), the research team will design and implement a set of management strategies for heterogeneous electric vehicle fleets, which utilize real-time data from various electric vehicles to improve the overall performance of heterogeneous electric vehicle fleets.