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Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm.

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options.

Recent trends in plug-in electric vehicles (PEVs) include the investment in public charging stations (PCS) construction, wherein PEVs can charge their batteries, and PCS owners can make profits. Scheduling algorithms used in the PCS can optimally assign PCS resources to the vehicles in order to maximize their profit. Although the main purpose of the PCS is to provide charging capability, it needs to address other customer requirements such as different deadline constraints, battery swapping, and vehicle to grid (V2G). In this paper, we introduce a comprehensive model for multiservice PCS profit maximization in which battery charging, discharging, and swapping services are included. The model has been used in formulating a mixed-integer linear programming (MILP) problem wherein charging and discharging of vehicles are scheduled to maximize the PCS profit. We utilize queuing theory to analyze the performance of the model at the presence of random variables such as solar power, electricity price, state of charge (SoC), amount of discharge (AoD), battery capacity, and vehicle arrival time to the PCS. Low complexity and near-optimal online scheduling algorithm is proposed to complete the offline scheduling solution. Our simulation results show that more profit for the PCS and less delay for vehicles are better achieved when the number of vehicles requesting battery swapping service is increased. Extensive simulations are conducted to evaluate the impact of different parameters on long-term profit and the performance of the online algorithm.

As dynamic inductive power transfer for electric vehicles is growing in relevance, it is important to analyze solutions towards its deployment and integration in the cloud-based services for electric mobility. In this paper we present an Internet-enabling platform for Electric Vehicle Supply Equipment, which features a high-level Charging Station Control Unit and a Power Electronics Controller. The platform is a middleware that controls the charging process taking into account outside world information. Tests were performed in a safe driving track, in Italy, to verify the effectiveness and robustness of the installation, for one year, for a total of 120 drive hours, under various weather conditions. Tests showed the suitability of the platform in terms of ability to authenticate and authorize a vehicle even through a remote service, sequentially control each coil in a lane, monitor the charging process, assist the driver in keeping the vehicle aligned so as to maximize the energy exchange and deliver charging session information to the cloud (e.g. for billing).

Direct vehicle-to-vehicle (V2V) charge sharing system has the potential to provide more flexibility to electric vehicle (EV) charging without depending on the charging station infrastructure or building designated parking lots. It can also provide an opportunity to shift peak time utility load to off-peak times. However, the assignment between the EVs that demand energy and the EVs with surplus energy or existing charging stations is a challenging problem as it has to be performed in real time considering their spatio-temporal distribution, availability, and grid load. In this paper, we study this assignment problem specifically in a supplier non-intrusive scenario (without changing their mobility) and aim to understand the potential benefits of a direct V2V charge sharing system. To this end, we present two new algorithms to match the demander EVs to suppliers for charging. In the first one, the maximum system benefit is targeted under multiple system objectives with different priorities and considering the grid load. In the second one, individual EV priorities are taken into account and a stable matching process depending on predefined user preferences is provided. We conduct extensive simulations using real user commuting patterns and existing charging station locations in three different cities together with a newly developed probabilistic EV charging behavior model and EV mobility. Simulation results show that direct V2V charge sharing can reduce the energy consumption of EVs by 20%–35% by providing a closer charging facility compared to the charging station only case, offload grid charging power in peak times by 35%–55%, and help sustain up to 2.2× EV charging requests without building new charging infrastructure, while causing a marginal increase (i.e., 3%–9%) in the energy cycling of supplier EVs. The results also show that the proposed matching algorithms offer 80%–90% more energy consumption reduction compared to the intrusive V2V charging at designated parking lots.

The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) has sparked considerable interest in academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid network, vehicles and charging stations can, respectively, make better charging and pricing decisions via real-time information sharing. In this paper, we study the charging problem in an intelligent transportation system, which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location, while the objective of a charging station is to maximize its revenue, given the charging strategy of the vehicles. We employ a multi-leader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that the equilibrium of the followers’ subgame played by the vehicles exits, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable using the proposed charging scheme. Finally, the performance of the proposed approach is demonstrated via extensive trace-driven simulations.

Plug-in electric vehicle (PEV) commercialization propels an extensive charging station deployment in a power distribution system to satisfy fast growing PEV charging demands. Considering the power distribution, some stations deployed at limited capacity feeders may undergo power overload at peak hours due to time-varying traffic and PEV demands. The potential power overload could lead to severe transformer degradation or even black-out on the aged power infrastructure. To avoid power overload without excessive expenditure on the infrastructure upgrade, proper energy compensation at limited capacity station is highly effective. In this paper, we investigate an energy compensation problem based on utility-owned mobile vehicular electric storage (MVES), aiming to mitigate the overload issues among a group of charging stations (GCS). First, a Markov Chain based energy capacity model is developed to estimate the energy statuses among GCS and a graph theory based GCS transportation model is developed to facilitate on-road MVES allocation. Then, a two-tier energy compensation framework is introduced to efficiently schedule MVESs to minimize the scheduling cost. Simulations are conducted based on real traffic data on California highway collected by California department of transportation, and the results validate the effectiveness of the introduced framework.

Inductive charging is based on the operating principles of a transformer; energy is transferred through an air-gap from a primary coil incorporated in the charging station to a secondary coil incorporated in the electric vehicle (EV). Dynamic inductive charging allows the vehicle to wirelessly receive energy while moving on the road and appears to be an ideal solution for the increase of the limited travel range of EVs. However, currently developed dynamic inductive power transfer (IPT) solutions limit the application of this method when the EV rapidly moves over the charging station. The aim of this paper is to propose a novel optimization procedure, in order to define the values for the variables involved in the electrical operation of a dynamic IPT system. The optimization procedure considers constraints associated with the operational conditions in the primary and secondary sides of the IPT system, in order to define the movement section, where the energy transferred to the EV is maximized at a considerably high system efficiency. The values derived from the optimization method are implemented in the control of the secondary side of the IPT system. Moreover, a control method is proposed that effectively eliminates the need for a detection mechanism and the continuous data transfer between the primary and secondary side. The suggested control scheme requires the operation of the charging station at a low-efficiency movement section without significant energy losses in this specific section. The proposed optimization procedure is applied in the case of a 60-cm-diameter circular pad, and the suggested control scheme is validated by simulating the operation of the dynamic IPT system over the whole EV movement range. An experimental setup has been used to study the application of the optimization procedure and investigate the effective operation of the system.

The lack of charging facilities has been a main obstacle to the widespread use of electric vehicles (EVs). What is worse is that existing chargers are still underutilized. Meanwhile, the grid instability caused by EV charging is becoming much more significant with increasing EV penetration. This paper studies how to size and locate charging stations in traffic networks considering grid constraints to balance the charging demand and power network stability. First, a spatiotemporal model of charging demand is proposed, and a $\text{1}-(\text{1}/e)$ approximation algorithm to maximize the charging demand is designed. We analytically prove that $\text{1}-(\text{1}/e)$ is the best bound that can be obtained in polynomial time. Then, a linearized power network model (LPNM) is proposed. Based on LPNM, a heuristic algorithm involving the grid constraints (HAG) is designed. Finally, the proposed models and algorithms are evaluated on real-world traffic networks and power networks. The relative error of the voltage deviation estimated by LPNM is about 4%. Compared with the plain demand model, adopting the spatiotemporal charging demand model improves the utilization of chargers by 5% at least. Compared with the greedy algorithm with grid constraints (GAG), HAG improves the carrying capacity of the power network by 20.7%, reduces the voltage deviation by 25%, and increases the EVs charged by 18.07%.

We consider a promising application for electric taxis (eTaxis) in transportation cyberphysical systems. The new rideshare scheme introduced herein takes into consideration both the limited battery of eTaxis and the user requirements. In the proposed eTaxi-sharing system, a passenger may share a taxi with others to enjoy a reduced fare and can potentially transfer from one eTaxi to another before reaching her destination, as long as her total trip time is within the maximum she specifies to be tolerable. Transfers are restricted to only take place at the designated (safe and convenient) battery charging stations scattered around the city. When an eTaxi comes to a charging station to pick up/drop off a transfer passenger, the eTaxi's battery can be charged. In this paper, we address a new optimization problem called Transfer-Allowed Shared eTaxis (TASeT), whose goal is to schedule an eTaxis service and find optimal rideshare and transfer plans to maximize the system throughput in terms of the number of passengers served by the taxi service within a given time period. A mixed-integer programming (MIP) formulation is presented to solve TASeT, along with an efficient greedy heuristic. Aside from large-scale simulation, we also present a case study that utilizes real taxi traces collected from the city of Shanghai, China. Compared with the nontransferable taxi-sharing (NTT) case, our solution could improve the number of served passengers and shared travel distance by 22% and 37%, respectively, during rush hours.

Fast-charging stations are connected to the electric grid and can fully charge an electric vehicle (EV) in less than half an hour. The capacity and location of the charging stations bring the costs to the electric grid operator, as well as to the station owner and EV user. A zonal approach has been proposed in this paper to determine the optimal place and capacity of fast-charging stations. Station development cost and the expected costs incurred by the EV user and the grid operator due to EV charging have also been included in the proposed approach. The geographic characteristics associated with the electric substations, urban roads, and city zones have also been considered in the proposed approach. EV user behavior is also considered to determine the expected charging demand and the expected EV user cost. The expected cost associated with extra grid loss due to EV charging has also been calculated by ac power flow using hourly electric grid load scenarios. The problem is formulated as a mixed-integer nonlinear (MINLP) problem to minimize the total expected EV charging costs. The problem is solved using the genetic algorithm technique to determine the optimal location and capacity of charging stations. The proposed approach has been applied to study charging station development for the northwest area of Tehran, Iran, and the robustness of the proposed approach has been evaluated.