Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved in the problem formulation. Based on the charging request information, we propose two charging guidance strategies from different perspectives. One of the strategies considers the travel demands of EV drivers and uses the driving distance as the optimization criterion. In contrast, the other strategy focuses on the impacts of EV number on the charging station operation and service satisfaction. The reachable charging stations with minimum vehicle number are selected as the optimal ones. More importantly, both the strategies have the ability to ensure the reachability of selected charging stations in a time-varying road network. In addition, we conduct simulation examples to investigate the performance of the proposed charging guidance strategies. Besides, the insights and recommendations on application scenarios of the strategies are introduced according to the simulation results under various parameter scenarios.
Supported by the technical development of electric battery and charging facilities, plug-in electric vehicle (PEV) has the potential to be mobile energy storage (MES) for energy delivery from resourceful charging stations (RCSs) to limited-capacity charging stations (LCSs). In this paper, we study the problem of using on-road PEVs as MESs for energy compensation service to compensate charging station (CS) overload. A price-incentive scheme is proposed for power system operator (PSO) to stimulate on-road MESs fulfilling energy compensation tasks. The price-service interaction between the PSO an…
A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation
Univ. Grenoble Alpes, CNRS, Grenoble INP（格勒诺布尔阿尔卑斯大学、法国国家科学研究中心、格勒诺布尔理工学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the loc…
The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometimes pressure on power grids. Uncoordinated charging of EVs may result in stress on distribution networks, and often some form of optimization is required in the charging process. Optimal coordinated charging is a multi-objective optimization problem (MOOP) in nature, with objective functions such as minimum price charging and minimum disruptions to the grid. In this manuscript, we propose a general multi-objective EV charging/discharging schedule (MOEVCS) framework, where the time of use (TOU) …
Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EVs adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi'an (Ch…
Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid. Left unmanaged, uncoordinated charging of electric vehicles (EVs) at increased levels of penetration will amplify existing peak loads, potentially outstripping the grid's capacity to reliably meet demand. In this paper, we report findings from the OptimizEV Project - a real-world pilot study in Upstate New York exploring a novel approach to coordinated residential EV charging. The proposed coordination mechanism seeks to harness the latent flexibility in EV charging by of…
The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. An MPC based optimization model is developed to consider future arrival of EVs as well as energy and regulation prices. The index of CVaR is used to model risk-averseness of an EVA. Simulations on the 1000-EV test system validate the effectiveness of our work in achieving a lucrative revenue while satisfying the charging requests from E…
This paper addresses optimal battery thermal management (BTM), charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimisation problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modelling the driving dy…
We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities. Notably, due to real-world constraints, we designed our framework around 3 main requirements. First, the smart charging strategy is readily deployable and customizable for a wide-array of facilities, infrastructure, objectives, and constraints. Second, the online optimization framework can be easily modified to operate with or without user input for energy request amounts and/or departure time estimates which allows our framework to be imple…
In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES mana…
The race for road electrification has started, and convincing drivers to switch from fuel-powered vehicles to electric vehicles requires robust Electric Vehicle (EV) charging infrastructure. This article proposes an innovative EV charging demand estimation and segmentation method. First, we estimate the charging demand at a neighborhood granularity using cellular signaling data. Second, we propose a segmentation model to partition the total charging needs among different charging technology: normal, semi-rapid, and fast charging. The segmentation model, an approach based on the city's points o…
Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize …
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 …
Route Optimization of Electric Vehicles based on Dynamic Wireless Charging
University of Thessaly（色萨利大学）；De Montfort University（德蒙福特大学）；Nottingham Trent University（诺丁汉特伦特大学）；University of Surrey（萨里大学）；T.E.I. of Thessaly（色萨利技术教育学院）
This paper investigates the problem of bidirectional energy exchange between electric vehicles (EVs) and road lanes embedded with wireless power transfer technologies called wireless charging-discharging lanes (WCDLs). As such, EVs could provide better services to the grid, especially for balancing the supply-demand, while bringing convenience for EV users, because no cables and EV stops are needed. To enable this EV--WCDL energy exchange, a novel decentralized peer-to-peer (P2P) trading mechanism is proposed, in which EVs directly negotiate with a WCDL to reach consensus on the energy price a…
Electric vehicles (EVs) have been growing rapidly in popularity in recent years and have become a future trend. It is an important aspect of user experience to know the Remaining Charging Time (RCT) of an EV with confidence. However, it is difficult to find an algorithm that accurately estimates the RCT for vehicles in the current EV market. The maximum RCT estimation error of the Tesla Model X can be as high as 60 minutes from a 10 % to 99 % state-of-charge (SOC) while charging at direct current (DC). A highly accurate RCT estimation algorithm for electric vehicles is in high demand and will …
Joint Transportation and Charging Scheduling in Public Vehicle Systems - A Game Theoretic Approach
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Electrical Engineering Department, Columbia University（哥伦比亚大学电气工程系）；Department of Electrical Engineering, Columbia University（哥伦比亚大学电气工程系）
The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. A model predictive control (MPC) based optimization is developed to consider the future arrival of EVs as well as energy and regulation prices. The index of conditional value-at-risk (CVaR) is used to model the risk-averseness of an EVA. Simulations on a 2000-EV test system validate the effectiveness of our work in achieving a lucrative…
This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On …
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considerin…
In this letter, we consider a transportation network with a 100\% penetration rate of connected and automated vehicles (CAVs) and present an optimal routing approach that takes into account the efficiency achieved in the network by coordinating the CAVs at specific traffic scenarios, e.g., intersections, merging roadways, and roundabouts. To derive the optimal route of a travel request, we use the information of the CAVs that have already received a routing solution. This enables each CAV to consider the traffic conditions on the roads. The solution of any new travel request determines the opt…
Lately, increasing number of electric vehicles (EVs) in residential parking station has become an important issue, because excessive number of EVs can destabilize the power system during peak hours with high charging power requested. When the power system of the residential parking station takes the structure of microgrid (MG), power provision for the EVs requires efficient power management scheme. To minimize the maintenance cost of the MG and maintain the grid stability, the MG needs to balance the charging/discharging power of EVs in the parking station. To achieve these goals, this paper p…
As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles have received worldwide attention. For convenient centralized management, taxis are considered as the fleet with electrification priority. In this work, we focus on the study on electric taxis dispatching, with consideration of picking up customers and recharging, based on real world traffic data of a large number of taxis in Beijing. First, the assumed electric taxi charging stations are located using the K mean method. Second, based on the station locations and the order demands, which a…
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…
The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vul…
Electric vehicles (EVs) are finally making their way onto the roads, but the challenges concerning long charging times and impact on congestion of the power distribution grid are still not resolved. Proposed solutions depend on heavy communication and rigorous computation and mostly need real-time connectivity for optimal operation; thereby, they are not scalable. With the availability of historical measurement data, EV chargers can take better-informed actions while staying mostly off-line. This study develops a distributed and data-driven congestion detection methodology together with the Ad…
Between-Ride Routing for Private Transportation Services
Institute for Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会科学研究所，麻省理工学院）；Department of Electrical Engineering & Computer Science, MIT（电气与计算机工程系，麻省理工学院）；Laboratory for Information and Decision Systems, MIT（信息与决策系统实验室，麻省理工学院）
