ABSTRACT

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 this project, 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. If successful, the research team will develop a clear understanding of how to manage large-scale heterogeneous electric vehicles to improve urban mobility efficiency from a fleet-oriented perspective, with potential applications to future autonomous electric vehicles. Such an understanding of heterogeneous electric vehicles will improve the quality of every-day life such as more efficient commutes and lower energy usage, which will benefit the environment.

To date, researchers have accumulated abundant knowledge on how to manage individual electric vehicles, even homogeneous electric vehicle fleets, based on precise mathematical models. Nevertheless, such models are over-simplified as they do not consider the cyber-physical hybrid state space or model uncertainties for heterogeneous electric vehicle fleets. Heterogeneous electric vehicle fleets are mobile cyber-physical systems with heterogeneous properties, for example, mobility patterns, energy consumption, and incentives to accept control decisions. However, the research community has a limited understanding of how to make network control decisions for heterogeneous mobile cyber-physical systems at large scale in a real-world setting. In this project, the research team aims to investigate the fundamental theories and applications to manage heterogeneous mobile cyber-physical systems by utilizing electric vehicles as an example platform. Specifically, the research team will (i) develop a set of data-driven cyber and physical models to predict the essential status of heterogeneous electric vehicle fleets, for example, mobility patterns and energy consumption rates and (ii) establish a hierarchical control framework to achieve performance guarantees for heterogeneous electric vehicle dispatching and charging management by developing data-driven distributionally robust optimization methods for hybrid systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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