Location Of Charging Stations Research Articles

ELECTRIC VEHICLE CHARGING STATIONS OPTIMIZATION: A LITERATURE REVIEW AND PROSPECTS FOR SOPHISTICATION

In contemporary society, depletion of fossil fuels and their impact on environment have shed light on the importance of moving toward vehicles that run on alternative-fuel sources. Among those vehicles the most promising and cutting edge in terms of sustainability and technology respectively are electrical vehicles (EVs). The adoption of EVs is considered a key driver on the way to the reduction air pollutions from the transport sector and improving environment in general. Proper development and deployment of EV infrastructure in cities across the world is paramount of importance for successful penetration of EVs in the market. This challenge has led researches to investigate possible ways for optimal location of EV charging stations that play a crucial role in the success of EV infrastructure. The optimal location for EV charging stations depends on a number of factors that include not only costs associated with charging EVs but also psychological issues such as range anxiety of EV drivers. In the literature, a variety of approaches, stochastic and deterministic variables, constraints and objective functions have been used in formulation of mathematical models that tried to address factors affecting integrity of EV infrastructure. In this article a review on the models - formulated on the basis of EV user approach -for optimal locating charging stations are demonstrated. Moreover, we present current research gaps and discuss possibilities that can be leveraged in further researches with respect to location selection of charging stations. Lastly, we show variables that should be considered in the formulation of models that are vital for increasing penetration of EVs in the market.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles (EVs) continues to grow and the demand for charging infrastructure is also increasing, how to improve the charging infrastructure has become a bottleneck restricting the development of EVs. In other words, reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system. Considering the construction and maintenance of the charging station, the distribution network loss of the charging station, and the economic loss on the user side of the EV, this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function, and thus proposes a location and capacity planning model for the EV charging station. Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm, the simulated annealing immune particle swarm optimization algorithm (SA-IPSO) is adopted in this paper. The simulated annealing algorithm is used in the global update of the particle swarm optimization (PSO), and the immune mechanism is introduced to participate in the iterative update of the particles, so as to improve the speed and efficiency of PSO. Voronoi diagram is used to divide service area of the charging station, and a joint solution process of Voronoi diagram and SA-IPSO is proposed. By example analysis, the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost, while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%. The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1% and basically does not fall into local convergence.

Set cover model-based optimum location of electric vehicle charging stations

Set cover model-based optimum location of electric vehicle charging stations

Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid

Electric vehicles (EVs) have attracted considerable attention because of their clean and high-energy efficiency. Reasonably planning a charging station network has become a vital issue for the popularization of EVs. Current research on optimizing charging station networks focuses on the role of stations in a local scope. However, spatial features between charging stations are not considered. This paper proposes a charging station selection method based on the graph convolutional network (GCN) and establishes a charging station selection method considering traffic information and investment cost. The method uses the GCN to extract charging stations. The charging demand of each candidate station is calculated through the traffic flow information to optimize the location of charging stations. Finally, the cost of the charging station network is evaluated. A case study on charging station selection shows that the method can solve the EV charging station location problem.

Deployment Optimization Strategies for Electric Vehicle Charging Stations

In recent years, with the gradual depletion of fossil energy and increasing environmental pollution, electric vehicles (EVs) have developed rapidly as one of the main substitutes for traditional fuel vehicles. Based on this, this paper firstly establishes a comprehensive optimization objective function including geographic information, charging time and charging amount. On the basis of determining the objective function, a quantum particle swarm optimization algorithm is proposed to solve the problem of optimal charging pile location and its number distribution. Secondly, a prediction model for the spatiotemporal distribution of electric vehicle users' charging demands based on fuzzy inference algorithm was proposed, and the charging load distribution curves of different functional areas were obtained by using Monte Carlo simulation method, which solved the spatiotemporal distribution of users' charging demands. Then, from the perspective of users, and taking into account the two-way cost of vehicle owners and charging station operators, a method of charging station location and capacity based on electric vehicle charging probability model is further proposed, which solves the problem of gradual expansion or reduction of charging piles. Finally, a multi-objective optimization model is established to solve the problem of optimal solutions for charging and battery swapping.

Public charging station localization and route planning of electric vehicles considering the operational strategy: A bi-level optimizing approach

• A two-phase heuristic approach combining a two-layer genetic algorithm and simulated annealing is presented. • A case study is carried out using data from a large city in China. • The proposed method provides could reduce the total delivery cost by 15%. In recent years, electric vehicles (EVs) have increased considerably in the logistics sector with the implementation of greenhouse gas (GHG) emission regulations. However, the driving range of EVs is limited by the battery capacity compared to combustion engine vehicles. This study proposes a public charging infrastructure localization and route planning strategy for logistics companies based on a bilevel program. A two-phase heuristic approach combining a two-layer genetic algorithm (TLGA) and simulated annealing (SA) is presented to solve the problem. The hybrid method uses TLGA to derive the optimal routing and charging plan and the SA descent algorithm is used to select the charging station (CS) locations. The proposed method is tested and compared to meta-heuristics using benchmark instances with charging stations. A case study is carried out using data from Chengdu, a major city in southwest China, to simulate the charging demand of public charging infrastructures. The proposed method provides more feasible allocations for public CSs and route planning, which could reduce the total delivery cost by 15%. This study demonstrates the potential of a bilevel optimizing approach to provide an optimized solution in citywide CS location selection and logistics routing problems.

A Novel Data-Driven Approach for Solving the Electric Vehicle Charging Station Location-Routing Problem

Due to increasing rates of adoption of electric vehicles (EVs), there is a strong need to deploy the necessary charging station infrastructure, together with routing strategies to manage traffic flow and congestion. This study addresses the location-routing problem (LRP) for a general EV charging system with stochastic charging requests regarding their locations, arrival times and charging times. The objective is to develop an efficient routing strategy of EVs to charging stations, as well as to determine the optimal charging station locations so as to minimize the demand’s mean response time. Under some regularity assumptions on the mean waiting time at each charging station (e.g. system operates in a light or heavy traffic regime), we show that the optimization problem can be formulated as a partition-based clustering problem with size constraints. This relaxation of the problem formulation enables us to develop a novel data-driven approach for solving the charging station LRP, without requiring detailed stochastic models for the EV’s charging requests, as well as the queueing behavior of the charging stations. An algorithm along with two size adjustment strategies are developed to solve the obtained clustering problem and illustrated on urban areas of Seattle with various types of distance, vehicle speeds, distributions for charging request locations, and inter-arrival time densities.

Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System

Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented.

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

Large-scale adoption of Robotaxi requires a comprehensive charging infrastructure as a guarantee, but the problems of insufficient capacity, low utilization, and unreasonable deployment of electric vehicle charging stations (EVCSs) are still common. In this context, this study proposed a multi-stage optimization strategy consisting of fleet sizing, charging demand simulation, model construction and solution to achieve the location and capacity optimization of EVCSs for the electric Robotaxi fleet. In stage one, a vehicle shareable network model based on graph theory was developed to determine the minimum Robotaxi fleet size required to adequately meet user travel demand, which was solved using the Hopcroft-Karp algorithm. In stage two, the specific spatio-temporal distribution of fleet charging demand was obtained by Monte Carlo simulation, considering the decision-making characteristics of Robotaxi operation and charging process. In stage three, a charging station location and capacity optimization model was established with the objective of minimizing the comprehensive costs, and an improved particle swarm optimization algorithm applying genetic operators to improve the population diversity was proposed. Finally, the effectiveness of the proposed model and algorithm was analyzed and discussed based on a case study using the real passenger order and geographic data from the city of Chengdu, China.

A Large Scale Group Three-Way Decision-based consensus model for site selection of New Energy Vehicle charging stations

The development of New Energy Vehicles (NEVs) has contributed to the alleviation of environmental pollution in the transportation sector. Although NEVs have some advantages, such as energy saving, being environmentally friendly, and low noise, they are restricted by their cruising range or recharging-related problems. To minimize such disadvantages, an optimal plan for the charging station site is necessary. This paper proposes a Large-Scale Group Decision-Making (LSGDM) method to select the best location for such charging stations. This method involves a large group of experts providing their preferences according to their knowledge and background. To facilitate the elicitation, our method uses Hesitant Fuzzy Linguistic Term Sets (HFLTS) and Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) model to improve the elicitation process, guarantee precise computing processes and obtain interpretable results. A social network is then constructed based on experts’ preference similarity and trust relationships, which reflects both their relationship and its strength simultaneously. Afterwards, a social analysis-based clustering process groups the experts, and a Three-Way Decision (TWD)-based Consensus Reaching Process (CRP) is introduced to improve the group’s agreement. Finally, a selection of charging station site case studies is conducted, and a comparative analysis is carried out to illustrate the quality of the proposal.

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

AbstractThis study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon‐neutral transition of transportation. This paper illustrates a two‐stage stochastic programming model capturing the uncertainty of PV power outputs and designs a step‐wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L‐shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step‐wise solution approach can provide high‐quality solutions within acceptable computational time. A case study is performed using a real‐world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems.

UAV Charging Station Placement in Opportunistic Networks

Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.

Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations

The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.

Research on multi-objective planning of electric vehicle charging stations considering the condition of urban traffic network

As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the location and capacity optimization model of the charging station with the goal of maximizing the revenue of operators and minimizing the user’s charging additional cost. At the same time, the road time-consuming index is introduced to quantify the impact of road congestion on the user’s charging additional cost, so as to effectively improve the user’s satisfaction during charging. Then, aiming at the charging station planning model, a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) based on chaos initialization and arithmetic crossover operator is proposed. Finally, taking the Haidian District of Beijing as the simulation object, the results show that compared with the situation of urban traffic networks not considered, the model proposed in this paper significantly reduces the cost of lost time of users by 11.4% and the total additional cost of users’ charging by 7.6%. It not only ensures the economy of the system, but also effectively improves the charging satisfaction of users, which further verifies the feasibility and effectiveness of the model, and can provide a reference for the planning and layout of charging stations in the future.

A Q-learning based electric vehicle scheduling technique in a distribution system for power loss curtailment

Electric vehicles (EVs) are becoming more common in the distribution network (DN), which burdens the system throughout the charging cycle. However, if EVs are used to assist the utility according to its demands, the scenario turns profitable. In this paper, a new methodology to schedule the EVs and distributed generation (DG) according to the DN system demand is proposed. The proposed method is a hybrid of the intelligent Q-learning algorithm, Loss Sensitivity factor (LSF), and Enhanced Grasshopper Optimization (EGOA) techniques. The main objective of this work is to encourage peak shaving, minimize the power loss and improve the voltage profile of overall DN in presence of EVs. The modelling of EVs is done by considering the most significant parameters such as EV State of Charge (EV SoC), travel itinerary, capacity of EV battery, and charging/discharging levels. Initially the location of EV charging stations and DGs are determined using LSF technique. Further peak shaving of DN considering EVs behaviour is implemented using Q-learning based smart charging method. Later the sizes of DGs are determined using EGOA technique. EGOA is an enhancement in the already existing grasshopper optimization technique that improves the algorithm's exploring capability. The hybrid approach helped in identifying the strong buses for EV charging station and DG placement. Further a peak shave of overall DN demand is achieved using Q-learning technique. Later using EGOA the DG sizing along with optimal EV placement helped in reduction of power loss by around 67% compared to base case. The voltage profile of DN is enhanced with the proposed hybrid approach without using compensation devices.

Internet of things based real-time electric vehicle and charging stations monitoring system

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%).

Siting charging stations and identifying safe and convenient routes for environmentally sustainable e-scooter systems

E-scooters are considered a new type of micromobility that promotes sustainable transportation modes by shifting the private car mode to a Mobility as a Service. Irregular parking and high life cycle global warming impacts of e-scooters due to dockless operating system cause a tendency from dockless systems to charging stations. This article presents one of the first comprehensive spatial analysis studies aiming to develop a novel GIS-based multi-criteria decision support model for: (1) siting optimum e-scooter charging station locations that will integrate e-scooter system with the existing public transportation systems and point of interests, and (2) finding the most secure and convenient road network to connect the charging stations. Identifying the optimum road network for connecting the charging stations and developing a raster based spatial method for this purpose distinguish this study from the previous studies. The developed model was implemented by performing a case study in Karsiyaka, Izmir, Turkey. 35 suitable locations were determined for siting e-scooter charging stations and the optimal connectivity network connecting each station with its neighboring stations were identified. The model can be reproduced for other locations and used to develop innovative policies and plans for environmentally more sustainable and operationally effective shared e-scooter systems.

A multicriteria GIS-based decision-making approach for locating electric vehicle charging stations

Electric vehicles (EVs) will be the next technological leap for urban mobility, however the market penetration rate depends on several factors, including the major hurdle of limited EV charging stations. Equity is also an important factor – ensuring EV charging stations are widespread and available for all segments of the population. This paper outlines an innovative methodology to systematically determine the locations of EV charging stations while considering equity and efficiency to maximize accessibility and usage. The methodology has two levels. First, solving what is known as the Set Covering Location Problem (SCLP) by determining a threshold so that the distance (or travel time) between the consumer and the EV charging station will be less than or equal to a given value. This is a policy-based decision and provides a framework to ensure EV charging stations are ubiquitous and equitable. Second is solving the Maximum Covering Location Problem (MCLP) by considering a series of evaluation criteria to satisfy the demand of the early adopters. Following evaluation of the SCLP and MCLP, selected locations are aggregated by partitions to develop larger scale hubs. These hubs will not only include EV charging stations but will also function as connection points that integrate different modes of transportation.

Routing and charging of electric vehicles: Literature review

An electric vehicle, as the name suggests, runs purely on electricity provided by battery packs located under its floor. This electricity fuels the engine and converts electric power to mechanical motion. Accordingly, electric vehicles do not emit pollutants as no combustion process takes place within, making it an eco-friendly alternative to combustion vehicles. However, electric vehicles face one major issue and that is their limited range. Recharging electric vehicles’ batteries requires an important amount of time. Since time is an important factor, recent works started focusing more and more on optimizing the use of recharge within an electric vehicle to make the most of it while avoiding frequent stops at the charging stations. Another riddle to solve, is the routing of vehicles which gets more complicated when electric vehicles are thrown into the mix. Not only an electric vehicle has a limited range, adding charging stations location to the problem affects the final path, the time and the cost of the routing problem. To help researchers advance in this direction, our work discuss the recent methods used to solve the vehicle routing problem, as well as the issues facing charging of electric vehicles. This will help establish a picture of where improvements need to be made and inspire future works to accelerate the rate at which electric vehicles will be roaming our roads. Our review shows that literature focuses increasingly on adapting electric VRP to real life settings by adding more and more constraints. Moreover, recent works are considering the recharging patterns of electric vehicles in order to reduce the overall travel time, distance and cost. The results of this review prove that complexity of electric VRP stems from the difficulty of satisfying all imposed constraints, as well as the inability to predict the outcome of the solution proposed face to real-life scenarios.

Optimal en-route charging station locations for electric vehicles: A new modeling perspective and a comparative evaluation of network-based and metanetwork-based approaches

In many regions across the world, the distribution density of electricity-charging stations is yet sufficient for taming the range anxiety concern of electric vehicle drivers. In such driving circumstances, making detours to find alternative charging opportunities is often an operational remedy to accomplish long-distance trips. To reduce the frequency and extra mileage of detouring, public charging infrastructures should be coordinately sited and constructed in a regional network. With the goal of minimizing the possible detour mileage, this paper describes an optimal charging station location problem for intercity highway networks and develops two approaches for modeling and solving it. Specifically, two mixed linear integer programming models are constructed in different network modeling paradigms: The first one is formed and solved on the basis of the original node-link network topology, with newly introduced integer variables such as path and subpath activation indicators to quantify the utilization status of paths and subpaths, respectively. The classic branch-and-bound algorithm encapsulating a bi-criteria label-correcting algorithm is designed for solving this network-based model. The other one is formulated and solved relying on the station-subpath metanetwork topology, as the result of a two-phase process that decomposes an individual routing and charging decision into two parts. The two phases are conducted on the node-link network and station-subpath metanetwork levels, respectively, where the first phase is done by solving a network-based, distance-constrained minimum-cost path problem, while the second phase collapses to a metanetwork-based, simple minimum-cost path problem. The findings we obtained through conducting this research are twofold: First, analytical and computational studies clearly identify the effectiveness of the two modeling and solution approaches, while the metanetwork-based approach exhibits its appealing computing advantage for solving problems of large size; second, the application of the solution approaches for a real-world network instance reveals how budget limit and distance limit impact the charging station locations, individual routing and charging decisions, and network cost and accessibility levels.