Joint optimization of UAV dual-task co-track and charging station location in large-scale IoT scenarios

Global climate change is affecting human life more seriously than ever before. Countries around the world have identified cars as a significant source of pollution, leading to an increased interest in eco-friendly automobiles. Electric vehicles (EVs), which are characteristically eco-friendly, have become a choice for future transportation system. In this paper, we proposed a strategy for determining the appropriate placement of large-scale charging stations using K-mean algorithm. Also, the initial results were validated utilizing actual electricity usage data from the existing chargers. Currently in Korea, a typical public charging station has two to three chargers to support EV users. As EVs become more popular, new problems arise, such as charger hopping and/or long waiting lines. In order to address these new issues, a large-scale charging station concept which houses more than ten chargers, is suggested. In doing so, a strategic approach for selecting close-to-ideal locations for the charging stations is introduced to maximize the charging station’s effectiveness. In this study, Jeju Island, Korea, which has many EVs, was used as a testbed. With the wealth of EV chargers and their usage data, initial validation of the proposed methodology was made possible. During the evaluation of the best possible locations for the largescale charging stations, we considered the locations of tourist attractions and convenient support facilities, as well as the population. After the evaluations, the proposed locations were validated using actual long-term charger usage data on Jeju Island. The demonstrated strategy for identifying appropriate locations for large-scale charging stations can be used by other tourist heavy islands or even small countries.

ABSTRACTBattery electric vehicles (BEV) are increasingly seen as one of the most suitable alternatives to internal combustion engine vehicles. An important issue involved in the dissemination of BEV relates with the deployment of the respective charging network and, in particular, with the locations of charging stations. Typically, the locations selected correspond to popular places such as city centers, shopping areas, train stations, and university campuses. Although these places are highly visible, the low parking times and high rotation rates often observed there could deliver an inadequate solution for the daily charging needs of users. In this article, a new approach to determine the location of BEV charging stations in urban areas is proposed. The approach relies on a detailed analysis of BEV charging needs, charging coverage, and adoption potential, as well as on an innovative type of location-allocation model. The usefulness of the approach is demonstrated for a set of hypothetical instances replicating the essential ingredients of real-world charging station location problems.

As a way of green travel, electric vehicles have significant advantages in protecting the environment and saving resources. However, imperfect charging station location planning will reduce the utilization of charging infrastructure, making it difficult for electric vehicles to charge in a timely and reasonable manner, which will affect the application and promotion of electric vehicles. Therefore, it is very important to plan the location of charging station legitimately. Aiming at the location of public charging stations, this paper proposes a location optimization method for public charging stations, which improves the charging efficiency through scientific and equitable planning of charging station locations and increases the vehicle's mileage on the road. This paper analyzes the driving and charging behavior of drivers from taxi trajectory data, and evaluates the impact of different charging station capacity settings on the positioning results. Among them, we use an immune algorithm to find the best location of public charging station. The experimental results show that the method can make effective decisions in determining the location of charging stations, which helps to provide certain practical significance for the location decision of urban public charging stations.

The method of route optimization of electric vehicle

In cities of low level of electromobility, it is particularly important to plan possibly the most efficient distribution of first-established charging stations. Since it contributes to building trust to electric vehicles, locations of charging stations should correspond to the actual needs of users to promote electromobility and maximize its implementation effect. The article presents a decision-making support method that helps to determine locations of the first charging stations in a given area. The method is based on the assumption that charging stations are set up at existing petrol stations. The method has been applied for the area of the city of Sosnowiec.

Due to rapid development of the economy, resource scarcity and environmental contamination are becoming increasingly significant. Electric vehicles have become a main direction for positive development. Charging stations are an energy supplement infrastructure for electric vehicles. How to provide reasonable electric vehicle charging station locations and capacity plans for different regions is the theme of this article. Based on the idea of gradual cover and the location of the charging stations, we propose a distance satisfaction function to improve the model. As the supporting infrastructure for electric vehicles, the location of electric vehicle charging stations has highly important significance for the promotion of electric vehicles. To solve the problem of location selection of an electric vehicle charging station and fully cover the demand point, we used the set coverage model and distance satisfaction function to propose a set coverage model based on distance satisfaction.

Electric vehicles are becoming more popular because they are not only helping countries’ economies by lessening dependency on oil but also helping in creating clean environments that are more habitable and sustainable. One of the most crucial issues in promoting the usage of electric vehicles is the availability of charging stations (CS). This study proposed to use GIS-MCDM methods to produce a model that could be used for selecting appropriate locations for EVCS. MCDM AHP, and FAHP techniques will be used to weigh the criteria regarding accessibility and influence on environment. Four main criteria are selected for this work these are environmental, geographical, urbanity, and transportation, the weight of different criterion will be determined. The results will be integrated into GIS for the selection of various suitable locations for EV charging stations. TOPSIS will be used to rank the sites and choose the best locations for charging stations. At the end of the study, it is expected to have a reliable model for the selection of suitable locations for EVCS, which could be used for the selection of proper location for EVCS, for efficiency and effectiveness of electric vehicles charging within cities and along highways, this will improve the adoption and acceptability of EV across the world.

This paper introduces a new hybrid technique for investigating the optimal location of electric vehicle charging stations. Reactive power compensation is also provided along with charging station to deal with power loss issues and retain the distribution network’s reliability. Moreover, vehicle to grid facility has been thoroughly examined in this article. The suggested algorithm is the hybrid of gray wolf optimization (GWO) and particle swarm optimization (PSO). The hybridization of two algorithms combines the desirable attributes of both algorithms and maintains a strong balance between the exploration and exploitation capability. The proposed hybrid approach is applied on IEEE-33 bus and 34-bus distribution systems to minimize the active power loss, maximize the net profit, and improve the reliability of electric grid network. Furthermore, the outcome attained using the anticipated approach is equated with conventional GWO and PSO. For 33-bus system, the proposed technique results in loss reduction of 30.67% and maximizing the net profit by 1142 $ and 1560 $ as compared to GWO and PSO, respectively. Likewise, power losses have been reduced to 27.6% and maximizing the net profit by 1726 $ and 3149 $ as compared to isolated GWO and PSO, respectively, for 34-bus network. The achieved outcomes prove the supremacy of the suggested approach over GWO and PSO to evaluate locations of charging stations’ and reactive power source in radial distribution network.

Growing concerns about environmental issues have led to the consideration of alternatives to current mobility. Electric mobility is one such alternative that is receiving a great deal of attention in Europe. In particular, a new legal framework for the introduction of an electric mobility system in Portugal has recently been set up by the government. A key issue in this system is recharging the batteries and, consequently, the location of charging stations. This paper presents a study on the location of electric-vehicle charging stations for an area of Lisbon, the capital city of Portugal, characterized with a strong concentration of population and employment. This type of area is appropriate for slow charging because vehicles stay parked for several hours within a 24-h period. The methodology used here is based on a maximal covering model to optimize the demand covered within an acceptable level of service and to define the number and capacity of the stations to be installed. The results clearly indicate that this methodology can be useful in the future planning of electric mobility systems.

With the rapid development of charging-while-driving technology, the deployments of charging lanes and direct current fast chargers (dcFCs) inevitably affect the charging and route choices behavior of battery electric vehicle (BEV) drivers. This article describes the first attempt to develop a predicted user equilibrium model on the road network considering both dcFC and charging lanes charging choices. This article develops a two-stage algorithm: First, we design a stated preference survey to seek the most significant attributes to attract BEV drivers to use a charging lane. Based on that, the second stage constructs a network equilibrium model: Given the locations of public charging stations and charging lanes, we then assigned the BEV traffic flow to a network based on their charging preferences. As an illustrative example, we conducted a numerical analysis on a road network to demonstrate the effectiveness of the model and solution algorithm. The results suggest that income and travel cost are the most significant attributes of the choice behavior and that the locations of charging stations and charging lanes have a significant influence on the route choices of drivers.

Due to a shortage of fuel sources and the increment in environmental pollution, efficient techniques should be introduced. The best solution is to move to the use of electric vehicles. The article aims to develop a solution for electric vehicle (EV) charging station locations that utilize the internet of things (IoT) technology. The IoT is a paradigm that uses sensors and transmitting networks to provide current facilities with a real-time global communication perspective of the physical world. This paper proposes a real-time system to provide a real-time update to EV location and charging stations (CSs) location to reduce time lost by users searching CSs, and provides real-time charging station (CS) recommendations for EV users by displaying the nearest CS, provide estimation arrival time to the nearest CS, display distance between nearest CS and EV real-time updated. The work of the proposed system was tested, and the most significant error rate (17 meters) is represented by the difference in the distance obtained from the system and the distance obtained from Google Map. The total accuracy of the design for the tested case is (98.014%).

The Active and Reactive Power Dispatch for Charging Station Location Impact Factors Analysis

Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet

The electric vehicle is seen as an effective way to alleviate the current energy crisis and environmental problems. However, the lack of supporting charging facilities is still a bottleneck in the development of electric vehicles in the Chinese market. In this paper, the cloud model is used to first predict drivers’ charging behavior. An optimization model of charging stations is proposed, which is based on waiting time. The target of this optimization model is to minimize the time cost to electric vehicle drivers. We use the SCE-UA algorithm to solve the optimization model. We apply our method to Dalian, China to optimize charging station locations. We also analyze the optimized result with or without behavior prediction, the optimized result of different numbers of electric vehicles, and the optimized result of different cost constraints. The analysis shows the feasibility and advantages of the charging station location optimization method proposed in this paper.

Charging stations deployment is an important problem in Electric Vehicle (EV) networks. The distribution of EV is complicated in urban environments. Therefore, reasonable location deployment will avail to reduce construction costs and improve user experience. Aim to this, this paper comprehensively considers the cost of charging stations and the charging costs of EVs. Studied the charging station location, charging station capacity and the optimization algorithms for charging station location, and proposed a method for estimating the optimal location and optimal capacity allocation of EV charging stations. Firstly, this paper uses the Voronoi diagram to divide the service range of the charging stations, then uses the differential evolution algorithm combined with the particle swarm optimization algorithm (DEIPSO) to solve the charging station location model, and finally consider the residence time of EV in the charging station, use queuing theory to solve the charging station capacity allocation model. The experimental results shows that DEIPSO can better jump out of the local optimum and achieve the global optimum; the proposed model can plan the charging station on the basis of fully considering the total charging costs of charging stations and EVs.