Network Of Charging Stations Research Articles

Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs

Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.

Network Modeling and Analysis of Internet of Electric Vehicles Architecture for Monitoring Charging Station Networks—A Case Study in Chile

Network Modeling and Analysis of Internet of Electric Vehicles Architecture for Monitoring Charging Station Networks—A Case Study in Chile

A Sustainable Solution for Urban Transport Using Photovoltaic Electric Vehicle Charging Stations: A Case Study of the City of Hail in Saudi Arabia

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system.

Optimizing electric vehicles charging for enhancing environmental sustainability and reducing carbon emissions of freight transport: case of Czech Republic

The limited infrastructure of charging stations, which is crucial in route planning and total journey time and creates uncertainty in efficiency and operating costs, calls for new economic and statistical methods in sustainability development and environmental economics. This paper aims to examine the challenges of integrating electric vehicles into freight transport to improve distribution logistics’ environmental sustainability, which represents one of the pathways for reducing environmental risk. The analysis results underscore the inadequacy of the truck charging station network in the Czech Republic. This insufficiency presents an opportunity to enhance environmental sustainability and reduce carbon emissions through strategic analysis and optimizing charging station locations. The difficulty of identifying optimal locations for these stations, given truck availability, requires using multi-criteria decision-making techniques such as the Analytical Network Process (ANP). Municipalities with limited access to existing logistics facilities were considered during the simulation. This way, 15 new locations were identified for municipalities with insufficient distance to a charging station.By implementing the ANP method, the study contributes to a more environmentally sustainable transportation infrastructure, highlighting the potential for significant reductions in carbon emissions through improved charging station networks. These results apply to other countries and can provide novel insights on optimizing charging station locations for sustainable economic development and reducing freight transport’s carbon emissions and environmental risks.

Germany's public EV charging points: Analysing the 2023 state of expansion

AbstractThe European Climate Law sets a legally binding target of net zero greenhouse gas emissions by 2050. In this regard, in 2023 the EU Environmental Council decided that from 2035 onwards only CO2‐neutral new cars will be licensed in the European Union. Simultaneously, since the energy crisis caused by the Russian invasion of Ukraine, fuel prices increased in Germany. This has made more and more households think about purchasing an e‐car. However, not every household has the opportunity to install their own wallbox, and when travelling long distances, e‐car users are dependent on the availability of ample public charging stations. Against this background, with a special focus on rural areas, this paper considers the questions whether and where spatial inequalities in the accessibility of public charging stations might currently exist. To approach these questions, based on an empirical analysis of the 2023 official location data of public charging points in Germany, we examine the nationwide distribution, accessibility and available capacity of public charging stations in Germany at small‐scale from the point of view of the ‘household’ as well as those who travel by e‐car within Germany. In order to evaluate the location data of public charging stations we mainly use raster‐based accessibility analysis methods as well as isotropic kernel intensity estimates. We found that, in contrast to the common belief, the network of public charging stations is quite dense. However, taking a closer look reveals that the charging infrastructure in Germany has so far been tailored to the needs of intra‐regional travelers (commuting, shopping, leisure) rather than to those of the residential population with no access to their own wallbox (daily recharging in close proximity to one‘s home) or the mid‐ to long‐distance travelers.

Research on Robustness of Charging Station Networks under Multiple Recommended Charging Methods for Electric Vehicles

Research on Robustness of Charging Station Networks under Multiple Recommended Charging Methods for Electric Vehicles

Evolutionary algorithms for dynamic optimization of plug-in charging station networks

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.

Decarbonization in Mexico by extending the charging stations network for electric vehicles

Climate change is an important phenomenon causing significant issues and concerns worldwide. As a consequence, many countries are making efforts to meet the goals proposed in the Kyoto Protocol and the Paris Agreement to reduce CO2 emissions. This paper explores the impact of electric vehicles on the decarbonization process in Mexico. Some scenarios are studied to propose an extension of the current charging stations network. These scenarios are modeled using a mathematical model that optimizes the location of such stations considering the origin-destination information available, a particular type of station, and different vehicle types and covered distances. The decarbonization impact is calculated based on the savings in CO2 emissions for the profile of electric energy generation in Mexico. The results for each scenario show that relevant coverage in the country can be achieved with a mild increase in the number of stations in strategic locations and with a benefit in the decarbonization effect.

Strategic Approach for Electric Vehicle Charging Infrastructure for Efficient Mobility along Highways: A Real Case Study in Spain

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.

Shared Energy and Reserve Based Coordinated Operation Between Electric Vehicle Charging Stations and Distribution Power Network

In this paper, we propose a coordinated operation method for a system containing electric vehicle charging stations (EVCSs) and a distribution power network (DPN), considering integrated energy and reserve regulation. In this method, we firstly propose a multi-objective optimization model of energy-reserve integrated decisions for the system considering the economic cost and the quality of service (QoS), which is based on the network calculus theory. Then, we adopt a characteristic function and a modified Shapely value method based on the coalition game to study allocations of economic benefits brought by the coordinated operation. Finally, simulation studies verify the effectiveness of the proposed method, by investigating the operation costs of EVCSs and the DPN in the non-coordinated and coordinated modes, respectively.

Design of electric vehicle charging station network on urban communities utilizing RSA-HBA technique

This paper proposes an electric vehicle charging station (EVCS) network design in urban communities using hybrid technique. The proposed approach consists of Reptile Search Algorithm (RSA) and Honey Badger Algorithm (HBA), which is jointly called as RSA-HBA technique.In this paper, a modeling method is embedded in the proposed technique to control the optimal design and sorts of electric vehicle distribution equipment for the community, considering the heterogeneity on demand and driver behaviors.Distance from home, drivers’ arrival patterns, willingness to walk, parking location and traffic on weekdays and weekends are certain important random data parameters deemed under this technique. To asymptotically coverage an optimal solution, the hybrid algorithm is used. A hybrid technique is proposed to direct the computational challenges for huge-scale phenomena. A detailed computational experiment is conducted to quantify the performance of the proposed technique. The performance of the proposed hybrid system is executed in the MATLAB platform and related with various methods.

Standard Load Profiles for Electric Vehicle Charging Stations in Germany Based on Representative, Empirical Data

Electric vehicles are becoming dominant in the global automobile market due to their better environmental friendliness compared to internal combustion vehicles. An adequate network of public charging stations is required to fulfil the fast charging demands of EV users. Knowing the shape and amplitude of their power curves is essential for power purchase planning and grid capacity sizing. Based on a large-scale empirical and representative dataset, this paper creates standard load profiles for various power levels, station sizes, and operating environments. It is found that the average power per charge point increases with rated station power, particularly for a rated power above 100 kW, and decreases with the number of charge points per station for AC chargers. For AC chargers, it is revealed how the shape of the power curve largely depends on the environment of a station, with urban settings experiencing the highest average power of 0.71 kW on average leading to an annual energy sale of 6.2 MWh. These findings show that the rated grid capacity can be well below the sum of the rated power of each charge point.

Charging infrastructure development in Russia: Incentives and prospects for the Vehicle-to-Grid technology use

Subject. This article discusses the issues related to the transportation electrification. Objectives. The article aims to analyze the economic aspects of the development of a network of power charging stations in Russia. Methods. For the study, we used general scientific research methods. Results. The article finds that in Russia at present, the tariff developed taking into account the charging of an electric vehicle during the hours of minimum electricity consumption, is the most economically profitable. Conclusions. The widespread use of electric vehicles requires the widespread development of electric charging infrastructure, as well as the use of tools to manage the demand for electrical energy.

Interurban charging station network: An evolutionary approach

In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm.

A Multi-Level Approach to the Target Development of the Electric Vehicle Charging Stations Network

The paper proposes an approach to managing a charging stations network for electric vehicles based on a combination of their properties as car service systems and infrastructure objects of the transport network. Within the framework of the study, a functional model of the charging stations network management system was built according to the IDEF0 structural modeling standard. The developed model made it possible to determine the main parameters and indicators of the specified management system. The current state of the system determines its configuration within morphological constraints. The optimal configuration is the basis for determining the rational structure of the charging stations network at a given level of detail. The mathematical formulation of the problem of multi-criteria optimization of the network structure was performed. The specified mathematical model represents the charging stations network as a mass service system and takes into account the requirements for its functioning at three target levels: client, business, society. The structure of the demand for charging station services and the client radius are strong arguments for the appropriate objective functions. Other arguments of the objective functions are presented through the configuration of the management system. An algorithm for optimizing the structure of the charging stations network has been developed based on the proposed social efficiency indicator. The considered approach is recommended for use in the process of designing and developing charging station systems, as well as forecasting their performance.

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.

On sustainable positioning of electric vehicle charging stations in cities: An integrated approach for the selection of indicators

Recent interest in planning a suitable charging infrastructure for electric mobility has motivated many studies. In this domain, attention towards key indicators of sustainable locations is crucial because they can provide informative signals to planning suitable charging station networks in cities according to multiple target attributes of sustainability. However, past literature has focused on “how to evaluate these key indicators of sustainable locations” by somewhat disregarding “which key indicators of sustainable locations were to be measured” based on the viewpoint of sustainability. This study presents a unified approach, identifying a long list of these indicators and then selecting a smaller set by applying an analytical hierarchy process, an algorithm, which aggregates weights and outcome marks, and a Monte Carlo simulation to an international survey. A relevant set of five overlapping key indicators of sustainable locations is designated by merging three different ranking methods. The results revealed a close semi-compensatory connection between transport and urban planning choices. These findings may support the planning of sustainable and suitable electric vehicle charging stations in cities according to a common set of key indicators of sustainable locations.

Metrological aspects of introducing charging stations for electric vehicles

Electric mobility is a rapidly evolving field, so electricity is considered one of the main alternative fuels. Electricity is a particularly attractive source of energy, which is used for electric vehicles (EVs) in urban/suburban agglomerations and other densely populated areas. An extensive network of EV charging stations is a major factor for using electric vehicles.
 The main method of EV charging is to connect the on-board charger to the AC power supply. An alternative method of EV charging is to connect the outboard charger to the DC power supply. There are three main options to connect an EV to a charging station: using a special power cable; with a pantograph; via wirelesses connection. A standardized interface between the electric vehicle and infrastructure seems to be one of the most topical challenges in the world of electric mobility.
 International standards cover payment accounting systems for electricity and provide specifications on the equipment and protocols for meter data exchange. There is an urgent need to draw up national or regional metrological regulations for EV charging. The issue of international and regional standardization of DC electricity meters for using at charging stations remains pressing.

Comprehensive evaluation of electric vehicle charging network under the coupling of traffic network and power grid

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.

Hidden Markov Models-Based Anomaly Correlations for the Cyber-Physical Security of EV Charging Stations

Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network.