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The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

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 $\alpha$ -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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

An energy trading system is essential for the successful integration of Electric Vehicles (EVs) into the smart grid. In this paper, leveraging blockchain technology, we first propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. The CS2V scheme is useful in crowded cities where there is a need for a charging infrastructure that can charge many EVs daily. We also propose a privacy-preserving vehicle-to-vehicle (V2V) energy trading scheme. The V2V scheme is useful when charging stations are not available or far and cheaper prices can be offered from EVs, e.g., if they charge from renewable energy sources. In the V2V scheme, the privacy of both charging and discharging EVs including location, time, and amount of power are preserved. To preserve privacy in both schemes, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads. [ABSTRACT FROM AUTHOR]

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep-learning-based approach provides effective battery charging control in multi-drone scenarios. [ABSTRACT FROM AUTHOR]

In an energy harvesting vehicular network, efficient file transfer between two charging stations is challenging due to the lack of communication infrastructure. Naturally, a source station can allocate the packets to a set of vehicles, which serve as relays to opportunistically deliver these packets to the destination station while performing their own tasks. In this paper, we show that, with packet-level erasure coding (e.g., fountain codes), only a few redundant packets are required to achieve a significant reduction in file delay. As more redundant packets are added, however, the file delay improvement diminishes, and the storage cost rises. In both the single and multiple-destination scenarios, the fundamental tradeoffs between coding and file delay are characterized. For the multiple-destination scenario, there exists a unique and optimal coding rate that minimizes the average file delay. We propose a greedy algorithm that can achieve this minimum delay in practice. Finally, the proposed tradeoffs are verified through intensive numerical experiments. [ABSTRACT FROM PUBLISHER]

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively. [ABSTRACT FROM AUTHOR]

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss. [ABSTRACT FROM PUBLISHER]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Integrating massive electric vehicles (EVs) into the power grid requires charging to be coordinated to reduce energy costs and the peak-to-average ratio (PAR) of the system. The coordination becomes more challenging when the highly fluctuant renewable energies constitute a significant portion of the power resources. To tackle this problem, a novel two-stage EV charging mechanism is designed in this paper, which mainly includes three parts. At the first stage, based on the prediction of future energy requests and considering the elastic charging property of EVs, an offline optimal energy generation scheduling problem is formulated and solved in a day-ahead manner to determine the energy generation in each time slot the next day. Then, at the second stage, based on the planned energy generation day-ahead, an adaptive real-time charging strategy is developed to determine the charging rate of each vehicle in a dynamic manner. Finally, we develop a charging rate compression (CRC) algorithm, which tremendously reduces the complexity of the problem solving. The fast algorithm supports real-time operations and enables the large-scale small-step scheduling more efficiently. Simulation results indicate that the proposed scheme can help effectively save energy costs and reduce the system PAR. Detailed evaluations on the impact of renewable energy uncertainties show that our proposed approach performs well in enhancing the system fault tolerance against uncertainties and the noises of real-time data. We further extend the mechanism to track a given load profile and handle the scenario where EVs only have several discrete charging rates. As a universal methodology, the proposed scheme is not restricted to any specific data traces and can be easily applied to many other cases as well. [ABSTRACT FROM PUBLISHER]

Aiming to improve the energy management in large areas for sustainable transportation, this paper models the energy demand of electric vehicles (EVs) in an urban area as a function of location and time. One important consideration for the energy supply of a charging infrastructure is the characterization of the local charge energy demand as a function of time. Spatiotemporal formulations are derived for a number of scenarios, ranging from individual vehicles to global area demands to describe their three-dimension dynamical features. Furthermore, the perceived charging demand is modeled by considering real situations, e.g., parking time, remaining battery energy, and arrival and departure patterns. The proposed charging urgency model describes the probability of charging actions and then computes the perceived energy need in an urban area. Additionally, detailed simulations and analyses have been performed based on a simulated city to show the essential features of the introduced energy demand models. [ABSTRACT FROM AUTHOR]

The paper considers a bidirectional power flow model of the electric vehicles (EVs) in a charging station. The EVs can inject energy by discharging via a vehicle-to-grid (V2G) service, which can enhance the profits of the charging station. However, frequent charging and discharging degrade battery life. We propose a menu-based pricing scheme, where the charging station selects a price for each arriving user (EV owner) for the amount of battery utilization, the total energy, and the time (deadline) that the EV will stay. The user can accept one of the menus or rejects all. The user reaches its decision based on the utilities. The charging utilizes its own limited renewable energy, and the conventional energy bought from the grid to serve the users. We show that there exists aprior-freepricing mechanism, which maximizes the ex-post social welfare for a myopic scenario. It selects a lower price for the menu containing a higher V2G service. However, theprior freepricing mechanism does not necessarily maximizes the expected profit. We show that the pricing strategy, which sets the price for a menu at a fixed value greater than the marginal cost of serving the menu, maximizes the expected profit for a wide class of utility functions. In the menu-based pricing, when the harvested renewable energy is small the users have higher incentives for the V2G service. We, numerically, show that the charging station's profit and the user's surplus both increase as V2G service is efficiently utilized by the pricing mechanism. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 \text1-(\text1/e) approximation algorithm to maximize the charging demand is designed. We analytically prove that \text1-(\text1/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%. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

The present world of vehicle technology is inclined to develop Electric Vehicles (EVs) with various optimized features. These vehicles need frequent charging which takes a longer time to charge up. Therefore, scheduling of vehicles in charging stations is required. Besides, the information of the EVs and its location is also stored by the charging stations and therefore creates a concern of EV privacy. Various researches are going on to solve these problems; however, an efficient privacy-preserving solution is less practiced till date. In this paper, a framework for Electric Vehicle (EV) charging is discussed. The framework uses the concept of Matching Market to identify a charging station and uses the lattice-based cryptography for secure communications. The matching market considers multiple factors to provide the best allocation of charging station and cryptography ensures security and privacy preservation. The use of lattice-based cryptographic hash SWIFFT avoids heavy computation. This usage of matching market and lattice cryptography, more specifically signcryption for EV charging framework are the highlights of the solution and add-ons to the novel features. Overall, the presented framework is efficient in terms of computation and communication cost, satisfaction ratio, slot ratio, charging latency and load balancing index. The performance metrics are compared with recent developments in this field. [ABSTRACT FROM AUTHOR]

Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for power grid, stimulates new ideas in current smart grid research. When coordinating a large number of EVs distributed in different geographical locations, a single aggregator is not sufficient to oversee the whole system and a hierarchical V2G system is required. Therefore, how to design a hierarchical V2G system and how to coordinate large-scale EVs to provide ancillary services become critical issues. In this paper, a generic hierarchical framework for a V2G system, which aims to provide frequency regulation services, is proposed to address the issues. Smart V2G aggregators (SVAs) are designed and employed to control the V2G system in a tree-like manner. A multi-level online V2G (MLOV) algorithm is devised for hierarchical V2G scheduling and it requires no forecasting information on regulation signals. It can also deal with the scalability issue encountered by the centralized algorithms and incast issue arising in the distributed algorithms. The simulation results show that the proposed algorithm outperforms the existing methods for the tradeoff between the quality of frequency regulation services and computational time. Through the computational study of the proposed algorithm, we also find that the computational time of the MLOV algorithm can be reduced exponentially by employing more SVAs and distributing the computational burden to the SVAs, with slight sacrifice on the smoothing quality. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal battery charging schedule for a long trip. Starting with a fully charged battery, the electric vehicle (EV) must stop at least once for battery charging before reaching its destination. Since the battery lifespan is thoroughly related to its depth-of-discharge and since the charging time can be long, it is therefore useful to provide a feasible battery charging schedule during the long trip. Minimizing a cost function, which includes the charging energy cost, battery degradation, and the charging duration, an optimal charging schedule is proposed and successfully validated with small-pickup EV data. In addition, this method takes into account the possible mass change, as well as wind effect on the predicted energy consumption. A comparative study with the commonly used maximum depleting and charging schedule suggests that the proposed approach is efficient, and it contributes to reducing the overall trip duration while reducing, at the same time, the battery degradation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We propose a battery replacement strategy to increase the efficiency of chargers and save drivers' time at charging stations for plug-in hybrid electric vehicles (PHEVs). With the battery replacement strategy, we optimize the scheduling for the operations of chargers in a charging station. The scheduling objectives are to minimize the power consumption cost and to allow as many PHEVs to be served as possible. In addition, we quantitatively analyze the impact of imperfect data communication on the scheduling performance. Furthermore, we formulate and solve a multistage stochastic programming model to minimize the power supply cost for the benefit of the grid operator. Renewable source generation and utilization of energy storage devices are integrated into the supply optimization problem. [ABSTRACT FROM PUBLISHER]

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between the electric vehicle and power grid thereby making the electric vehicle as a virtual power plant. This seamless exchange of energy flow is possible only when the entities involved communicate wirelessly in a secured manner. In order to ensure privacy-preserving in the V2G communication, development of a novel key establishment protocol is an active area of research. This article presents a lightweight mutual authentication and key establishment protocol for secure V2G communication among the electric vehicle user, charging station, and utility service provider. The correctness of the proposed protocol is verified using the formal method BAN logic, and many of its security threats are analyzed informally. Further, the protocol is compared for its security and efficiency with the related protocols in the literature under various aspects, and the comparison results show that our protocol outshines the others. The functional correctness of the protocol is also verified in Xilinx Zynq-7000 series FPGA board so that it can be deployed in any real-time applications. [ABSTRACT FROM AUTHOR]

Energy storage systems in recent days are witnessing an increased trajectory of hybridization to decrease the burden on the single energy storage systems in renewable energy sources. The hybridization of energy storage imposes the need for an efficient power-sharing strategy. This article proposes the interval type2 fuzzy logic controller-based power-sharing strategy to utilize hybrid energy storages in the solar-powered charging station effectively. Under this study, the hybrid energy storage system consists of a battery and supercapacitor with solar power generation as a primary source. The battery supports the slow varying power and the supercapacitor supports the fast-varying power. Electric vehicles are connected as load and a multi-step constant current charging technique is proposed to charge its battery. In order to inject/absorb the power, the hybrid energy storage utilizes bidirectional DC-DC converters. The perturb and observe algorithm is used to track the maximum power from the solar panel. The proposed system is designed in MATLAB/Simulink platform and the real-time validation has been carried out through the dSPACE (DS1202) real-time processor. An obtained result demonstrates the performance of the interval type2 fuzzy logic controller-based power-sharing strategy under variable generation and load scenarios. [ABSTRACT FROM AUTHOR]

In this article, a new mathematical formulation for the electric vehicle routing problem (EVRP) is proposed. This formulation extends the Green Vehicle Routing Problem (GVRP) considering time-of-use energy (TOU) prices, and including a detailed model for the EVs’ energy consumption. The main decisions for the considered EVRP are relevant to the choice among different types of charging modes at recharging stations, the speed of EVs, the loaded cargo and the battery charge. The model objective consists of minimizing the cost for the total travel distance and that for energy purchase, which depends on the selected recharging mode. A preprocessing algorithm used to reduce the problem dimension is presented. The experimental analysis performed on a large set of benchmark instances is reported. [ABSTRACT FROM AUTHOR]

The concept of smart city strives for greener technology to reduce carbon emission to ameliorate the global warming. Following this footprint, the transportation sector is experiencing a paradigm shift and the transition to electric vehicles (EVs) has prodigious plausibility in reducing carbon emission. However, the anticipated EV penetration is hindered by several challenges, among them are their shorter driving range, slower charging rate and the lack of ubiquitous availability of charging locations, which collectively contribute to range anxieties for EVs' drivers. Meanwhile, the expected immense EV load onto the power distribution network may degrade the voltage stability. To reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting time and shorter travel distance to charge. This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. We also propose two heuristic methods and compare our solution with them. Finally, a custom built Python-based discrete event simulator is developed to test our outcomes. [ABSTRACT FROM AUTHOR]

Electric vehicles are a promising instrument for reducing greenhouse gas emissions and other local pollutants, such as nitrogen oxides. Especially the taxi sector is a promising field of application. First, their high driving distances are necessary for electric vehicles to economize against conventional vehicles. Second, taxis are operated predominantly in city centers, where the pressure to reduce local emissions is greatest. However, while the electrification potential of taxi fleets has gained great attention in literature, the question of whether charging infrastructure can be profitable if only used by taxis has not been answered yet. To answer this question, we analyze 161 taxis in Karlsruhe, Germany. We find that a high share of electric taxis would come at lower cost than their diesel counterparts. Thus, a 25–45% share of taxi driving could be electrified, if only the taxi user perspective would be taken into account. However, while ten charging sites would be necessary to do so, only one charging site could be refinanced through exclusive taxi use. Consequently, the electrification potential would fall to about 3%, if only this one profitable charging site was built. Yet, in the future, electric driving will become cheaper. In 2025, already 55% electrification potential would be possible, given that both electric driving and charging infrastructure operation would be profitable. Altogether, while currently taxi charging infrastructure would require public funding or other business models, in the medium to long term, an exclusive use by taxis would be sufficient. [ABSTRACT FROM AUTHOR]

Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. There are currently two major ways of refueling electric taxicabs (ETs): recharging and battery swapping. ETs usually choose battery swapping so as not to waste precious time during their daily work. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. Furthermore, most ETs are fully charged in the early morning and need to swap battery in approximately the same time period of the day, which results in a bottleneck for battery swapping and hurts the quality of service (QoS) of the taxicab fleet. With this insight, we model the ET fleet as a mobile sensor network, analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile and the power consumption of individual taxis, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme, then describe a real-time algorithm to schedule a subset of the unoccupied taxicabs to swap batteries early by giving them allowances to avoid congestion. Our strategies are then evaluated via a real-world 366-day, 3 976-taxi dataset. The results demonstrate that compared to uniform deployment, our planning scheme reduces the average time cost by 67.2%; and based on our deployment, our scheduling strategy decreases the in-station queuing time by 45.63%, 52.26%, and 29.57%, and the average driving time cost to the stations by 42.3%, 43.69%, and 28.77% compared to actual taxicab refueling, random scheduling, and a static heuristic strategy, respectively. Furthermore, our approach reduces the number of battery swapping that last more than 30 min by 40.8%, which is a significant improvement on the QoS of the whole system. [ABSTRACT FROM AUTHOR]