CPS: Small: Collaborative Research: Models and System-Level Coordination Algorithms for Power-in-the-Loop Autonomous Mobility-on-Demand Systems

CPS：小型：协作研究：功率在环自主按需移动系统的模型和系统级协调算法

• 批准号: 1837135
• 负责人: Marco Pavone
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837135&HistoricalAwards=false
• 关键词: CPS; Small; Collaborative; Research; Models

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.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.

该项目的目标是研究自动驾驶电动汽车如何按需运送乘客（一种称为自动按需移动或AMoD的系统），以实现对电力和交通网络的优化、耦合控制。关键的观察是，AMoD技术将在电力和交通网络之间产生复杂的耦合，即人们在城市中移动时充电需求和电价之间的耦合。其假设是，通过控制和优化利用这种耦合，AMoD系统将降低发电成本，提高风能和太阳能等间歇性可再生能源的集成度，同时提供更方便的运输。该项目的结果将为交通运输利益相关者和政策制定者提供有关在社会规模上部署自动驾驶汽车的指导方针，通过培养清洁高效的未来交通系统，使美国经济受益。该项目将设计理论模型和优化工具，以表征上述耦合，并在环路中对电网进行AMoD的系统级控制。关键的技术思想是将耦合的电力和交通网络置于流优化的正式框架中，将城市区域、充电站和道路抽象为图的节点和边，将客户、车辆和能源的运动抽象为图上的流。然后，该项目将设计一个控制框架来优化决策变量，例如车辆路线、充电决策和发电计划。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand

用于按需可转移自主移动的图元强化学习

• DOI: 10.1145/3534678.3539180
• 发表时间: 2022
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Gammelli, Daniele;Yang, Kaidi;Harrison, James;Rodrigues, Filipe;Pereira, Francisco;Pavone, Marco
• 通讯作者: Pavone, Marco

Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting

• DOI: 10.1109/itsc48978.2021.9565089
• 发表时间: 2021-07
• 期刊: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
• 作者: J. Luke;Mauro Salazar;R. Rajagopal;M. Pavone

A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems

• DOI: 10.23919/acc45564.2020.9147734
• 发表时间: 2020-07
• 期刊: 2020 American Control Conference (ACC)
• 作者: Felix Boewing;Maximilian Schiffer;Mauro Salazar;M. Pavone

Joint Design and Control of Electric Vehicle Propulsion Systems

电动汽车推进系统的联合设计与控制

• 发表时间: 2020
• 期刊: European Control Conference
• 作者: Verbruggen, Frans;Salazar, Mauro;Pavone, Marco;Hofman, Theo
• 通讯作者: Hofman, Theo

On the Interaction Between Autonomous Mobility on Demand Systems and Power Distribution Networks—An Optimal Power Flow Approach

• DOI: 10.1109/tcns.2021.3059225
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Alvaro Estandia;Maximilian Schiffer;Federico Rossi;E. Kara;R. Rajagopal;M. Pavone