The COVID-19 pandemic has depressed overall mobility across the country. The changes seen reflect responses to new COVID-19 cases, local health guidelines, and seasonality, making the relationship between mobility and COVID-19 unique from region to region. This paper presents a data-driven case study of electric vehicle (EV) charging and mobility in the wake of COVID-19. The study shows that the number of EV charging sessions and total energy consumed per day dropped by 40% immediately after the arrival of the first COVID-19 case in Utah. By contrast, the energy consumed per charging session fell by just 8% over the same periods and the distribution of session start and end times remained consistent throughout the year. While EV mobility dropped more dramatically than total vehicle mobility during the first wave of COVID-19 cases, and returned more slowly, both returned to stable levels near their mean values by September 2020, despite a dramatic third wave in new infections.
In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling v…
We study the joint route assignment and charge scheduling problem of a transit system dispatcher operating a fleet of electric buses in order to maximize solar energy integration and reduce energy costs. Specifically, we consider a complex bus transit system with preexisting routes, limited charging infrastructure, limited number of electric buses, and time-varying electricity rates. We present a mixed integer linear program (MILP) that yields the minimal cost daily operation strategy for the fleet (i.e., route assignments and charging schedules using daily solar forecasts). We present numeric…
The first of this two-paper series proposes and elaborates a concept of electric vehicle (EV)-based e-mobility system. To this end, models designed to reap the benefits of EVs' flexibility in the literature almost exclusively consider charging stations as active players exploiting the EVs' flexibility. Such stations are seen as static loads able to provide flexibility only when EVs are connected to them. However, this standpoint suffers from two major issues. First, the charging stations need to anticipate some important parameters of the incoming EVs, e.g. time of arrival and departure, state…
The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and …
This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and t…
Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs that correlates with a value depreciation of the entire EV. As EV users' satisfaction depends on reliable and value-stable products, car manufacturers offer charging assistants for simplified and sustainable EV usage by considering individual customer needs and battery aging. Hitherto models to quantify battery aging have…
Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data
Massachusetts Institute of Technology（麻省理工学院）；Senseable City Lab, Massachusetts Institute of Technology（麻省理工学院感知城市实验室）；Department of Urban Studies and Planning, MIT（麻省理工学院城市研究与规划系）；Istituto di Informatica e Telematica del CNR（意大利国家研究委员会信息与电信研究所）
University of Connecticut（康涅狄格大学）；University of Illinois, Chicago（伊利诺伊大学芝加哥分校）；Florida State University（佛罗里达州立大学）；Rutgers University（罗格斯大学）；University of Virginia（弗吉尼亚大学）
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service an…
In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times. Specifically, we design a pricing policy to make the charging price dynamic and dependent on the traffic congestion, predicted via the cell transmission model, and the availability of charging spots. Furthermore, we develop a novel framework to model how this policy affects the drivers' decisions by formulating a mixed-integer potential game. Technically, we introduce the concept of "road-to-station" (r2s) and "stati…
In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vechicles (AMoD EVs). We consider the case where vehicles complete trips and then enter a between-ride state throughout the day, with their information becoming available to the fleet operator in an online fashion. In the between-ride state, the vehicles must be scheduled for charging and then routed to their next passenger pick-up locations. Additionally, due to the unknown daily sequences of ride requests, the problem cannot be solved by any offline approach. As such, we study an…
Uncoordinated charging of a rapidly growing number of electric vehicles (EVs) and the uncertainty associated with renewable energy resources may constitute a critical issue for the electric mobility (E-Mobility) in the transportation system especially during peak hours. To overcome this dire scenario, we introduce a stochastic game to study the complex interactions between the power grid and charging stations. In this context, existing studies have not taken into account the dynamics of customers' preference on charging parameters. In reality, however, the choice of the charging parameters may…
