Visible to the public CPS: Small: Collaborative Research: Models and System-Level Coordination Algorithms for Power-in-the-Loop Autonomous Mobility-on-Demand SystemsConflict Detection Enabled

Project Details
Lead PI:Mahnoosh Alizadeh
Performance Period:01/01/19 - 12/31/21
Institution(s):University of California-Santa Barbara
Sponsor(s):National Science Foundation
Award Number:1837125
62 Reads. Placed 508 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: The goal of this project is to investigate how self-driving, electric vehicles transporting passengers on demand (a system referred to as autonomous mobility-on-demand, or AMoD) can enable optimized, coupled control of the power and transportation networks. The key observation is that the AMoD technology will give rise to complex couplings between the power and transportation networks, namely couplings between charging demand and electricity prices as people move around a city. The hypothesis is that by exploiting such couplings through control and optimization, AMoD systems will lead to lower electricity generation costs and higher integration levels of intermittent renewable energy resources such as wind and solar, while providing more convenient transportation. The results of this project will provide guidelines to transportation stakeholders and policy-makers regarding the deployment of autonomous vehicles on a societal scale, benefitting the U.S. economy by fostering clean and efficient future transportation systems. This project will devise theoretical models and optimization tools for the characterization of the aforementioned couplings and for the system-level control of AMoD with the power network in the loop. The key technical idea is to cast the coupled power and transportation networks in the formal framework of flow optimization, whereby city districts, charging stations, and roads are abstracted as nodes and edges of a graph, and the movements of customers, vehicles, and energy are abstracted as flows over such a graph. This project will then devise a control framework to optimize over the decision variables, e.g., vehicles' routes, charging decisions, and power generation schedules.