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To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance. [ABSTRACT FROM AUTHOR]

Employees are increasingly using electric vehicles (EVs) as their choice of company car. Charging infrastructure is limited by undersized connection lines and a lack of charging stations on company premises. Upgrades require significant financial investment, time, and effort. Smart charging represents an approach to make the most of existing infrastructure while satisfying charging needs. The objective of smart charging depends on the business context. Objectives of interest include fair share maximization, electricity cost minimization, peak demand minimization, and load imbalance minimization. During business hours, EV arrivals and departures are predictable while still containing uncertainty. To utilize this knowledge ahead of time, this paper presents a novel approach for combining day-ahead and real-time planning for smart charging. First, we model the problem using mixed-integer programming for day-ahead planning to precompute schedules. Next, we propose a schedule guided heuristic which takes as input precomputed schedules and adapts them in real time as new information arrives. Both methods use a parameterized weighting mechanism to flexibly combine and emphasize individual objectives of smart charging. Experimental results from simulations show significant benefits of combining day-ahead and real-time planning over using a single planning approach in isolation. Improvements include increased fair share and decreased energy costs. [ABSTRACT FROM AUTHOR]

For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Research considers a general setup using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in urban areas where driving range is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we studied each feature to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this paper shows the importance of incorporating the protection device upgrade in urban system planning on charging stations. [ABSTRACT FROM AUTHOR]

Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation. [ABSTRACT FROM AUTHOR]

This paper presents an operating model of a station that charges a single-depot and homogeneous fleet of electric vehicles (EV) performing deliveries. Both the charging station and the fleet of EVs are owned by a single delivery company. The operating model, whose goal is to find the optimal EV battery charging schedule, is based on a clustering technique that keeps track of the number of EVs with a specific battery state of energy (SoE), while considering battery degradation, variable (dis)charging efficiency and nonlinear charging speed. The proposed operating model can be used both for the day-ahead scheduling and for the intraday model-predictive-control-based adjustments. Due to its relatively low capacity as compared to other market participants, the charging station is considered to be a price taker in both markets. The price uncertainty is considered using the robust uncertainty budget. The paper also evaluates inefficiency of a commonly used charging policy, i.e., the baseline model, where every delivery vehicle is charged to at least a predetermined SoE before its departure. The presented model is evaluated using multiple case studies and sensitivity analysis. As opposed to the non-clustered baseline model, the proposed approach scales well for very large fleets. Our analysis confirms that the results of the baseline model depend on the preset SoE at departure, while the proposed model provides optimal solution without assumptions on the departing SoE. [ABSTRACT FROM AUTHOR]

The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-first-served approach and some state-of-the-art ACO-based dynamic optimization algorithms. [ABSTRACT FROM AUTHOR]

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

One solution to the limited range of battery electric vehicles is the provision of a public charging infrastructure to enable longer journeys. This paper describes a simulation model of a routing and charge point reservation system based on the charging infrastructure deployed in Ireland during early 2016. Extensive Monte Carlo simulations are run using the Irish population density and an estimated trip length distribution. The results show quantitatively the advantages of a routing and charge point reservation systems on a country wide scale in terms of the number of electric vehicles that can be supported. The effect of charge point fault rates is also quantitatively assessed and shown to be a critical factor in public charging infrastructure deployment. [ABSTRACT FROM PUBLISHER]

This paper proposes an integrated electric vehicle (EV) charging navigation framework, which takes into consideration the impacts from both the power system and transportation system. The proposed framework links the power system with transportation system through the charging navigation of massive EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a hierarchical game approach is proposed in this paper to effectively navigate EVs to electric vehicle charging stations (EVCSs). At the upper level of the hierarchical game, a non-cooperative game is proposed to model the competition between EVCSs. Based on the pricing strategies obtained from the non-cooperative game, multiple evolutionary games are formulated at the lower level to evolve EVs’ strategies in choosing EVCSs. The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability of the power distribution grid and economic profits of the charging stations. [ABSTRACT FROM PUBLISHER]

A mobile charging system (MCS) for electric vehicles (EVs) is presented in this paper. The proposed MCS has a 2-MWh energy storage capacity for charging five EVs simultaneously with 75-kW individual charging rates. The MCS would be rechargeable from the available power substations (three phase, 480 V) with 375-kW charging rate. The appropriate modular power interface is designed and implemented between the MCS, batteries of the EVs, and the power substations. Experimental validation is presented in this paper to demonstrate the system performance and effectiveness under rapid charging requirements. [ABSTRACT FROM AUTHOR]

The coordinated development of charging points reveals to be crucial in supporting the envisaged diffusion of electric vehicles, along with their proper design to ensure smart and fast charging performances and economic advantages for drivers. The integration of charging points with local generation and storage can enhance this process. In this paper, the structure of a modular electric vehicle supply infrastructure is proposed. It is based on the integration of photovoltaic rooftops, energy storage and electric vehicle stations in a dc microgrid, enabling coordination and control as well as the functions of bidirectional charging and the possibility to provide grid services at connection point. The main features of the technical demonstrator are described, along with the targeted innovations for system manager and final user, and the possible control functions involving operation planning, real-time control, and ancillary service market inclusion. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers’ range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator’s profits and customers’ waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator’s daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate pricing policy. In order to avoid a long queue build-up at the FCS, a new pricing policy is then proposed. The intuition behind the scheduled pricing policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increasing price of fossil fuels and its decreasing resources on the one hand and environmental pollutions on the other hand, use of electric vehicles (EVs) has been increased. Charging EVs has imposed new loads on power systems. These new and major loads along with the deregulation of power systems, which introduces new uncertainties to grid, have caused new challenges for the frequency control and stability of power systems. Use of EVs as moving batteries is one of the ways for dealing with this problem. In this method, EV charging is controlled and, when necessary, EV battery is discharged in grid. This concept is so-called vehicle to grid (V2G). V2G concept is employed in this paper for the control of a smart deregulated grid frequency. For this purpose, an optimized fuzzy controller is used to control EVs. Using the proposed method, charging or discharging batteries is carried out with respect to grid frequency and battery state of charge. To investigate the proposed approach, a modified IEEE 39-bus system in the presence of renewable energy resources is assumed. Then, this system is converted into a three area system in order for the frequency analysis. Investigating the performance of the proposed method for the charging of EVs is done in another part of paper. Simulations are carried out in MATLAB/SIMULINK environment and their results illustrate good performance of the proposed method in the frequency control of deregulated system and EV charging. [ABSTRACT FROM PUBLISHER]

This paper presents a queuing analysis-based method for modeling the 24-h charging load profile of a plug-in electric vehicle (PEV) charging station. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day. The first PEV charging load profile assumes customer convenience as the factor that influences the hourly arrival rate of vehicles at the station, while the second profile is developed assuming that customers would respond to PEV charging prices and arrival rates are accordingly affected. One of the main contributions of this paper is to model the PEV service time considering different factors such as the state-of-charge of the vehicle battery, as well as the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined and compared using the stochastic optimal power flow and the model predictive control approaches. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user’s mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone-specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of $2\times6$ seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

With the increase of electric vehicle (EV) sales, the pricing strategies of public charging stations have significant impacts on their revenues and the spatiotemporal distribution of charging loads. In this paper, we quantify three kinds of price elasticity of charging demands based on the historical charging data of multiple public charging stations with different pricing schemes. The relationship between the volume-weighted average price (VWAP) and the corresponding total charging demand within a zone is studied, which does not require changing the charging prices to estimate elasticity. To evaluate the shifting of charging demands in different periods and zones, a conditional random field (CRF) model is built, which depicts the spatiotemporal correlations of charging demands. In this model, the VWAPs and the total charging demands are taken as observed variables and hidden variables, respectively. The loopy belief propagation algorithm is used to infer the loopy graph approximately, and the learning algorithm with forgetting factors is used to estimate the unknown parameters of the CRF model. The price elasticities are derived from CRF, and the elasticity matrices of charging demands are obtained. Computational results based on historical charging data verify the validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) have become popular in recent years as an essential requirement for safer and more efficient transportation systems. Internet of Electric vehicles (IoEV) as well as their hybrid forms provide an ideal means of supporting sustainability within an ITS. The control of charging/discharging of EV is still a challenge, despite the tremendous research progress to date in the field. In this paper, the use of charging station data and binary vectorization are proposed in order to provide timely insights on the dynamic behavior of charging processes. A Bag-of-Power-States model has been created for similarity measurement of charging stations within given time periods. The results of experimentations using synthetic data have shown that the proposed ${{Bag-of-Power-States}}$ model is computationally feasible and provides useful results for optimizing the scheduling of power supply to charging stations that may be located across a wide range of distances, over the same period of time. [ABSTRACT FROM AUTHOR]

This article presents a single stage isolated bidirectional DC–DC converter comprising of LCL-T resonant network for universal electric vehicle (EV) charging. Fixed frequency operation along with phase-shift control enables easier design of passive components, while achieving high efficiency across wide output voltage range. Furthermore, to increase the switching frequency and consequently the power density, gallium nitride (GaN) switches are explored in the proposed solution. In order to facilitate use of commercially available 650 V GaN transistors, a multi-level inverter and a reconfigurable rectifier architecture is utilized along with LCL-T resonant network, accommodating an input voltage of 800 V, generated from an universal three phase ac input supply, and an output voltage ranging from 150 V to 950 V catering to large variation of battery voltages from different vehicle manufacturers. The constant input voltage-to-output current gain property of LCL-T network, along with an efficient phase-shift modulation proposed in this paper enable soft switching of all transistors with minimum circulating currents over constant current (CC), constant power (CP), and constant voltage (CV) modes of battery charging. Experimental results are provided for a single-phase DC–DC converter prototype utilizing 650 V GaN transistors operating from an 800 V DC bus and providing a very wide output voltage from 150 V to 950 V at 6.6 kW maximum power at a constant switching frequency of 500 kHz. The prototype achieves two peak efficiency points of 98.2% and maintains $>$ 97% efficiency across the entire output voltage range at a power density of 120 W/in3 (7.3 kW/L). [ABSTRACT FROM AUTHOR]

This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: 1) deployment and operation costs and 2) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting genetic algorithm. Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the tradeoff between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution. [ABSTRACT FROM AUTHOR]

Mobility-on-Demand (MoD) systems using electric vehicles (EVs) are expected to play a significantly increasing role with urban transportation systems in the near future, to both cope with the massive increases in urban population and reduce carbon emissions. One inconvenience in MoD-EV systems is the need for some customers to perform in-routing charging for almost-out-of-charge EVs. In this paper, we propose a routing scheme that aims to reduce this inconvenience by minimizing the relative excess time spent by MoD-EV systems customers for in-route charging compared to the on-road trip time. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate our objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers relatively to their actual trip time without in-route charging. Both single and multiple charging units per charging station are considered in this paper and modeled as M/M/1 and M/M/c queues, respectively. For both types of queues, the optimal routing proportions are derived analytically using the Lagrangian analysis and the Karush–Kuhn–Tucker conditions. Simulation results show the merits of our proposed solution in both cases as compared to the shortest time and the random routing decisions. Finally, the proposed method is tested on a real-world scenario, and the computation times are calculated for different settings. [ABSTRACT FROM AUTHOR]

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results. [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 order to provide an efficient charging service to future electric vehicles (EVs), it is important for the charging station to estimate its available capacity. As the charging station usually has a fixed number of chargers and a limited charging capacity, it cannot accommodate all the arriving cars. Thus, a decision has to be made by the charging station whether a new arriving car can be admitted and then be scheduled for charging. This not only depends on the charging station’s physical constraints but also depends on how certain the charging station is toward its energy supply. We study the problem of EV charging using various energy sources (such as energy from the grid, renewable energy such as solar photovoltaic panels, and local energy storage) and their impacts on the charging station’s performance. Unlike the energy from the grid, solar generation varies with time and cannot be predicted precisely. To this end, we introduce a multiplier ${k}$ to measure the effect of solar prediction error and a composite performance index (the figure of merit) to capture the charging station’s utility, also taking account of EVs’ charging requirements. We further propose a two-stage admission and scheduling mechanism to find the optimal trade-off between accepting EVs and missing charging deadlines by determining the best value of ${k}$ under various energy supply scenarios. [ABSTRACT FROM AUTHOR]

A four-stage intelligent optimization and control algorithm for an electric vehicle (EV) bidirectional charging station equipped with photovoltaic generation and fixed battery energy storage and integrated with a commercial building is proposed in this paper. The proposed algorithm aims at maximally reducing the customer satisfaction-involved operational cost considering the potential uncertainties, while balancing the real-time supply and demand by adjusting the optimally scheduled charging/discharging of EV mobile/local battery storage, grid supply, and deferrable load. The chance-constrained optimization objective has been stated in stages: 1) stage I, optimization of day-ahead energy management schedules; 2) stage II, multitiered EV charging price update and optimization of discharging participation bonus; 3) stage III, optimization of hour-ahead energy management schedules; and 4) stage IV, real-time control. Such algorithm provides more resilience for unpredictable conditions, provides more incentives for EV users to participate, and better coordinates the integrated system including the building load to reliably serve the customers while lessening cost. Case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results indicate that the proposed algorithm can reduce the operational cost and at the same time provide higher tolerability toward uncertainties. [ABSTRACT FROM AUTHOR]

A battery charging station (BCS) is a charging facility that supplies electric energy for recharging electric vehicles’ depleted batteries (DBs). A BCS has a certain number of charging bays and maintains a dynamic inventory of fully charged batteries (FBs). This paper studies a BCS scheduling (BCSS) problem whose target is to schedule the charging processes of the charging bays such that the charging cost is minimized while satisfying the FB demand. Specifically, the BCSS problem has two types of operations: 1) loading DBs into the charging bays and then unloading them to the FB inventory when they are fully charged and 2) controlling the charging rate of each charging bay. We formulate the BCSS problem as a mixed-integer program with quadratic battery degradation cost. A generalized benders decomposition algorithm is then developed to solve the problem efficiently. The salience of the developed algorithm is that: 1) each charging bay can solve its own subproblem separately and 2) each subproblem can be further partitioned into multiple independent and identically structured quadratic programming problems, and thus the algorithm facilitates an efficient parallel implementation. We perform extensive real data simulation to validate the optimization model and demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand–supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs. [ABSTRACT FROM AUTHOR]

The hybrid electric bus (HEB) presents an emerging solution to exhaust gas emissions in urban transport. This paper proposes a multiport bidirectional switched reluctance motor (SRM) drive for solar-assisted HEB (SHEB) powertrain, which not only improves the motoring performance, but also achieves flexible charging functions. To extend the driving miles and achieve self-charging ability, photovoltaic (PV) panels are installed on the bus to decrease the reliance on fuelsbatteries and charging stations. A bidirectional front-end circuit with a PV-fed circuit is designed to integrate electrical components into one converter. Six driving and five charging modes are achieved. The dc voltage is boosted by the battery in generator control unit (GCU) driving mode and by the charge capacitor in battery driving mode, where the torque capability is improved. Usually, an extra converter is needed to achieve battery charging. In this paper, the battery can be directly charged by the demagnetization current in GCU or PV driving mode, and can be quickly charged by the PV panels and GCUAC grids at SHEB standstill conditions, by utilizing the traction motor windings and integrated converter circuit, without external charging converters. Experiments on a three-phase 128 SRM confirm the effectiveness of the proposed drive and control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the stochastic energy management of electric bus charging stations (EBCSs) is investigated, where the photovoltaic (PV) with integrated battery energy storage systems (BESS) and bus-to-grid (B2G) capabilities of electric buses (EBs) are included for cost-effective charging of EBs. Also, the day-ahead dynamic prices are derived to mitigate charging impacts on power distribution systems. This problem is formulated as a distributionally robust Markov decision process (DRMDP) with uncertain transition probabilities and costs to address the impacts of random bus loads with inaccurate probability density function estimation. An event-based ambiguity set with combined statistical distance and moment information is developed to achieve minimax-regret criteria for less-conservative and robust solutions. To facilitate practical applications with reduced computational complexity, a heuristic regret function is proposed, based on which the dynamic prices are derived. Case studies based on EB data from St. Albert Transit and IEEE test feeders indicate that the proposed method can minimize EB charging cost with mitigated impacts on power distribution systems. [ABSTRACT FROM AUTHOR]

With promising benefits such as emission reduction, traffic congestion alleviation and parking space saving, electric vehicle sharing systems have attracted increasing attentions. This paper proposes a Trip Pricing Scheme (TPS) for a large-scale EV-sharing network with Shared Electric Vehicle (SEV) demand prediction. In the proposed system, the SEV traffic demand is firstly predicted through a model which includes a cascade graph convolutional neural network and a long-short term memory neural network. Based on this, the TPS is modelled as a mixed-integer nonlinear programming problem, which aims at maximizing the total system profit from EV sharing business. The proposed TPS determines the optimal combination of the two price adjustment levels, which provides incentives to the spatial-temporal distribution of SEVs’ traffic flows to maximize the system’s profit. Numerical simulations are conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The study of future scenarios where transport systems are highly based on electric propulsion constitutes a key point for the world sustainable development. In particular, the integration between electric vehicles and smart grids makes possible reaching new goals on energy savings, efficiency of energy generation and distribution. This paper proposes a controller integrated into the electric vehicle, based on fuzzy logic, providing the car user with the necessary information regarding the charging/discharging stations located nearby. In particular, for each station a priority level is reported, representing the degree of economic convenience/necessity that the user has in using this charging station. The final objective is to easily integrate a fleet of electric vehicles into smart grids with users willing to charge and discharge their electric vehicles during periods and modalities that highly favors the smart grid operation, energy savings, and economic efficiency for the car owners. [ABSTRACT FROM AUTHOR]

This paper studies the joint problem of optimal pricing, charging scheduling and rebalancing for one-way car sharing systems. Firstly, a price driven customers travel mechanism is given which represents the customers ride demand as a linear function of the ride price. Then, a joint framework ensuring load balance and profit maximization of the system is introduced, in which the migration of vehicles and customers in the system is captured by fluid models, while the charging procedure of electric vehicles in the charging stations is described by multi-server queues. The well-posedness and equilibrium of the fluid model are analyzed, which yields the minimum fleet size needed. Next, a rebalancing method is derived to maximize the operating income while the stations operate at an equilibrium state, i.e., vehicles available and no waiting customers. To make the system better adapt to the changing customers demand in highly dynamic environments, a real-time rebalancing policy is developed by introducing a customer arrival predicting scheme based on exponential smoothing. Numerical experiments and case studies in Chengdu show that the proposed method can significantly improve the quality of service and rebalancing performance. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively. [ABSTRACT FROM AUTHOR]

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]

This paper evaluates the trend of fast dc charging systems (combining the trends of the infrastructure and of the vehicles) to verify the feasibility of the development of an infrastructure of transnational corridors that can guarantee the accessibility for all vehicles. This paper starts with the real-life situation characterized by multiple disjointed projects in many countries to verify whether, in spite of the different solutions employed, it is possible to find a common trend that can become a guideline for future projects. A predictive model for the e-mobility trend is developed to understand the market and the development of the infrastructure over the next few years. The results are used to identify how to equip European transnational corridors with fast dc charging stations, and a study case is presented. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a centralized electric vehicles (EVs) recharge scheduling system for parking lots using a realistic vehicular mobility/parking pattern focusing on individual parking lots. We consider two different types of EV based on their mobility/parking patterns: 1) regular EVs; and 2) irregular EVs. An extensive trace-based vehicular mobility model collected from the Canton of Zurich is used for the regular EVs, and a probabilistic pattern built on top of this trace is used for modeling the behavior of irregular EVs. To the extent of our knowledge, this is the first EV charging scheduling study in the literature that takes into account a realistic vehicular mobility pattern focusing on individual parking lots. We compare the performance of our proposed system with two well-known basic scheduling mechanisms, first come first serve and earliest deadline first, with regard to two objective functions: 1) maximizing the total parking lot revenue; and 2) maximizing the total number of EVs fulfilling their requirements. Comparison results show that our proposed system outperforms well-known basic scheduling mechanisms with regards to both objectives. Parking lots managing the recharging of a high number of EVs will greatly benefit from using such recharge scheduling systems in the context of smart cities. [ABSTRACT FROM PUBLISHER]

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart mobile devices to collect and share data. Traditional MCS systems mainly leverages the people carried smartphones and other wearable devices which are constrained by the limited sensing capability and battery power. With the popularity of unmanned vehicles like unmanned aerial vehicles (UAVs) and driverless cars, they can provide much more reliable, accurate and cost-efficient sensing services due to to their equipped more powerful sensors. In this paper, we propose a distributed control framework for energy-efficient and DIstributed VEhicle navigation with chaRging sTations, called “e-Divert”. It is a distributed multi-agent deep reinforcement learning (DRL) solution, which uses a convolutional neural network (CNN) to extract useful spatial features as the input to the actor-critic network to produce a real-time action. Also, e-Divert incorporates a distributed prioritized experience replay for better exploration and exploitation, and a long short-term memory (LSTM) enabled N-step temporal sequence modeling module. The solution fully explores the spatiotemporal nature of the considered scenario for better vehicle cooperation and competition between themselves and charging stations, to maximize the energy efficiency, data collection ratio, geographic fairness, and minimize the energy consumption simultaneously. Through extensive simulations, we find an appropriate set of hyperparameters that achieve the best performance, i.e., 5 actors in Ape-X architecture, priority exponent 0.5, and LSTM sequence length 3. Finally, we compare with four baselines including one state-of-the-art approach MADDPG. Results show that our proposed e-Divert significantly improves the energy efficiency, as compared to MADDPG, by 3.62 and 2.36 times on average when varying different numbers of vehicles and charging stations, respectively. [ABSTRACT FROM AUTHOR]

With the development of extreme fast charging technology, charging stations need to use energy storage stations to reduce the rising peak to average power ratio (PAPR). Lithium-ion capacitor (LIC) is a chemical power source that uses both Faraday process and non-Faraday process to store energy. Because of its attractive performance in terms of rate characteristics and chemical stability, it is suitable for some energy storage stations that consider both power density and energy density. It is important to describe the current-voltage characteristics of LIC to predict the charge and discharge efficiency in the early design of energy storage power stations. During the test, however, a full discharge or charge results in a high temperature rise, and the electrical model parameters near a specific temperature point cannot be accurately obtained. The short current pulses cannot stabilize the polarization. In this paper, a high-accuracy parameters identification method based on an improved Butler-Volmer-Equation-Based electrical model is used to summarize the phenomena caused by the rate of change of high-energy LIC. The accuracy of the method is tested under the dynamic stress condition test. The maximum voltage error is less than 2%. Energy efficiency calculation based on the used model is simulated by the design condition from the energy storage station of the Haizhu line in Guangzhou. The maximum error is less than 0.2%. [ABSTRACT FROM AUTHOR]

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]

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid. [ABSTRACT FROM AUTHOR]

Carsharing is a mode of transportation that provides access to a set of vehicles in the form of organized short-term car rental, serving as a substitute for private car ownership with a number of transportation, environmental, and social benefits. Combining the mobility concept of carsharing with electric vehicles (EVs), referred to as e-carsharing, can contribute not only to more efficient use of the shared vehicles, but also to more sustainable urban mobility in smart cities. In this context, this paper advances the concept of university-based e-carsharing, to serve the mobility needs of an academic community in Bilbao, Spain, focusing on the technical design aspects to cover the energy requirements of the EV fleet of the proposed system through the installation of fast charging posts based on a battery-to-battery approach. In this regard, a MATLAB/Simulink model is implemented to simulate the fast charging infrastructure using the real-world data collected from the university parking lot in order to represent the potential utilization of the EVs. The simulation results confirm the effectiveness of the proposed system design, ensuring that the energy demand of the EVs is successfully covered and concurrently the charging station batteries operate out of the low charge zone. [ABSTRACT FROM AUTHOR]

Energy storage and reactive power supplied by electric vehicles (EVs) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with an hours-ahead scheduling scheme. This paper introduces an optimization and control framework that can be used for charging batteries and managing available storage while using the remaining capacity of the chargers to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a robust distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and their application raise new questions and demands. Accordingly, the charging process of EVs is a novelty in everyday life when using such vehicles. This paper presents results of user charging behavior within a field test. In the field test, 500 people with above average yearly mileage took part in integrating the vehicles into their everyday lives. For the results of this paper, the charging behavior of 200 test people is taken into account. The differences in user behavior between battery electric vehicles (BEVs) and extended range electric vehicles (EREVs) are discussed. The results indicate that there are various opportunities for grid services based on EVs, as the vehicles are connected or parked close to a charging station the majority of the time. Moreover, the results also show deviating user behavior for the BEVs and EREVs. [ABSTRACT FROM AUTHOR]

The increasing number of electric vehicles (EVs) on highways calls for the installment of adequate charging infrastructure. Since charging infrastructure has limited capacity, EVs need to wait at a charging station to get charged, and their waiting times may differ significantly from one location to another. This paper aims at developing a strategy to coordinate the queues among the charging stations, with only local information about traffic flows and the status of EV charging stations along a bidirectional highway, so that excessively long waiting times can be avoided. Specifically, a distributed algorithm is presented to schedule EV flows into neighboring charging stations, so that EVs are all appropriately served along the highway and that all the charging resources are uniformly utilized. In addition, a distributed decision making policy is developed to influence the aggregate number of EVs entering any given service station, so that each EV makes an appropriate decision (i.e., whether or not it should enter the next charging station) by contributing positively to meeting the desired queue length at service stations and by considering its own battery constraint. Performance improvement of the proposed strategy is illustrated via one of the highways in the United States, namely the Florida Turnpike. [ABSTRACT FROM PUBLISHER]

The present paper describes an Electric Vehicle (EV) charging station that provides ancillary services to the main AC grid, as for example frequency support and synthetic inertia. Due to the Direct Current (DC) nature of these loads and the easier integration of renewable energies, a DC Microgrid is considered to power the charging station. Targeting both DC grid voltage stability and the capability to support the main AC grid, nonlinear control laws based on Lyapunov theory and backstepping are developed for local and primary control levels (droop control). The proposed controllers are validated via MATLAB-Simulink simulations in a 25 kW DC Microgrid with PV production, Vehicle to Grid (V2G) mode of operation, fast and slow vehicle charge, ancillary services to the AC main grid and islanded mode operation. [ABSTRACT FROM AUTHOR]