详情

A multi-AI-agent framework for vehicle-infrastructure integration and electric vehicle robust charging

项目名称：	A multi-AI-agent framework for vehicle-infrastructure integration and electric vehicle robust charging
摘要：	Transportation systems and in particular fossil fuels utilized in transportation are responsible for approximately 27% of greenhouse gases (such as Co2 emissions) in the U.S. in 2015. Under the Clean Air Act, more and more states are adopting California’s Zero Emission Vehicle (ZEV) regulations. This results in an increase in the number of electric vehicles (EVs). It is expected that EVs will comprise 30% of all cars globally by 2030. In addition to the pollution caused by the fossil fuels, the transportation system itself has the potential to greatly reduce emissions production and energy consumption through reducing congestion. Traffic congestion not just cause travel delays but also increases fuel consumption and emissions production. One of the major reasons for congestion in urban areas is traffic accidents. These crashes are the leading cause of accidental death in the United States with the major factor in over 90 percent of all fatal crashes being human error. Currently, traffic cameras and video surveillance are one of the ways used to monitor the traffic. However, these methods are capital demanding, and don’t provide real-time trip information to the travelers. New technologies, such as vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communication, may be able to greatly reduce congestion. This communication allows real-time detection of congestion, which can result in immediate distributing of traffic affected by the congestion and therefore result in a more efficient transportation network. Advances in wireless communication technology, for instance advanced 5G communication networks will enable this interconnection and will allow users to make better decisions regarding the use of the transportation system. In the foreseen transportation infrastructure, vehicles will communicate with other vehicles, traffic control units and traffic management centers, to make more efficient trip decisions. In addition to the communication reducing traffic congestion, it will also help in fast EVs charging, utility and capital cost management. Developing an effective management scheme that maximize the use of limited EV charge stations currently available is essential for transportation infrastructure agents, and utility companies to deliver high-quality service to travelers (availability of chargers, prices, reduced capital investment in new chargers, etc). This project proposes to develop a novel multi-agent artificial intelligence (AI) communication and charging system that will focus on (1) reducing traffic congestion and (2) smart EV charging. In this proposed management system traffic control units, traffic management centers, EV charging stations, utility companies and EVs are AI-powered agents capable of making smart decisions based on real-time traffic conditions, and EV charging demands and prices.
状态：	Active
资金：	80000
资助组织：	Office of the Assistant Secretary for Research and Technology
项目负责人：	Mousa, Momen
执行机构：	University of Texas at San Antonio
开始时间：	20200801
预计完成日期：	20220201
主题领域：	Data and Information Technology；Energy；Highways；Operations and Traffic Management；Vehicles and Equipment