Data Driven Modeling and Scheduling of Hybrid Wireless Power Transfer Charging Systems to Serve Electric Vehicles
Yan, Li, Computer Science - School of Engineering and Applied Science, University of Virginia
Shen, Haiying, EN-Comp Science Dept, University of Virginia
Intelligent Transportation System is a major application field for Cyber-Physical Systems (CPS). Future public transportation system will be featured by Electric Vehicles (EVs). However, due to battery capacity limit, the driving range of an EV is limited. To fulfill metropolitan transit demands, public transportation EVs are expected to be continuously operable without recharging downtime. Although there have been many previous mature works on plug-in cable charging systems, EVs must stop and get plugged in the charging points of the charging stations to get recharged, which wastes time and becomes an obstacle for the continuous operability of public transportation EVs.
Wireless Power Transfer (WPT) techniques that charge an EV when it is temporarily parked (stationary wireless charger) and in motion (dynamic wireless charger) is a solution. The key contribution of this dissertation is building a hybrid WPT charging system composed of stationary and dynamic wireless chargers to support the charging demands of a metropolitan-scale group of public transportation EVs. The designed methodologies for building the hybrid WPT charging system consists of (1) a stationary wireless charger deployment approach that utilizes spatial and temporal analysis of passenger appearance and a generic traffic model to both maximize the taxicabs' opportunity of picking up passengers at the chargers and support the taxicabs' continuous operability on roads with the minimal deployment cost. (2) a dynamic wireless charger deployment approach that utilizes categorization and clustering of traffic flow attributes and a generic traffic model to support the continuous operability of electric vehicles on roads with the minimal deployment cost; and (3) a taxicab dispatching and charging approach that utilizes customized selection and training of suitable historical passenger demand data and charging optimization to minimize the taxicab’s number of missed potential passengers due to charging. By saying suitable historical data, we mean the data that are under the influence of random factors (e.g., weather, holiday) similar to current passenger demand.
Through simulation based on a metropolitan-scale mobility dataset of public transportation vehicles, we demonstrate that our proposed methodologies for developing the hybrid WPT charging system can better serve public transportation EVs in terms of continuous operability, electricity utilization efficiency, and service efficiency.
PHD (Doctor of Philosophy)
Cyber-Physical Systems (CPS), Intelligent Transportation System, Wireless Charging System for Electrical Vehicle, Large-scale Mobility Dataset Analysis