• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.137-143
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• 2014

Car exhaust emissions are recognized as one of the key sources for climate change and electric vehicles have no emissions from tailpipe. However, the limited charging infrastructures could restrict the propagation of electric vehicles. The purpose of this study is to find the vulnerable districts limited to the charging station services after meeting the goal of Ministry of Knowledge Economy(12%). We assumed that the charging service can be provided by current gas stations. The range of the vulnerable grades was determined by the accessibility to current gas stations and the vulnerable regions were classified considering the optimal number of charging stations estimated by the efficiency function. We used 4,827 sub-municipal divisions and 11,677 gas station locations for this analysis. The results show that most of mountain areas are vulnerable and the fringe areas of large cities generally get a good grade for the charging infrastructure. The gangwon-do, jeollanam-do, gyeongsangbuk-do, and chungcheongnam-do include more than 40% vulnerable districts.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.210-219
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• 2017

Electric vehicles, which are attracting attention as eco-friendly vehicles, have been increasing in number since 2011 in Korea. The purpose of this study is to analyze the efficient operation of existing charging stations and factors to consider when installing additional charging stations based on the case of Jeju Island where the electric vehicle penetration rate is high and the charging infrastructure is relatively well established. The characteristics of using electric car charging stations by region, type of facility, and time of day are analyzed. As a result of analyzing the frequency of using the charger installed in Jeju Island, the utilization of both the fast charger and the slow charger is found to be concentrated in a specific area. The usage rate of charger installed in a business facility and a public parking lot is high in both fast charger and slow charger. However, according to the usage rate by time of day, the fast charger has a high utilization rate throughout the afternoon, while the use of a slow charger is concentrated in the morning. In order to enable users to utilize the electric vehicle charging station efficiently, it is necessary to provide a publicity guide for the charging station having a low utilization rate, a notice for using the charger, and a notification of completion of charging. Considering the charging demand, the area where the charger is not yet installed should be considered as the area to install the charger, and in addition, the additional installation should be considered in the area and the facility where the amount of charge is large. Service improvement is expected to be possible by utilizing actual electric vehicle charging behavior analysis result.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.60-61
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• 2019

Drones are opening new horizon as a major Internet-of-Things (IoT) player which is a network of objects. Drone needs to charge itself during providing services from the charging stations. If there are lots of drones and one charging station, then it is a critical situation to decide which drone should get charged first and make order of priorities for drones to get charged sequentially. In this paper, we propose an efficient scheduling algorithm for drone power charging (ESADPC), in which charging station would have a scheduler to decide which drone can get charged earlier among many other drones. Simulation results have showed that our algorithm reduces the deadline miss ration and turnaround time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.131-138
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• 2023

Globally, the eco-friendly industry is developing due to the climate crisis. Electric vehicles are an eco-friendly industry that is attracting attention as it is expected to reduce carbon emissions by 30~70% or more compared to internal combustion engine vehicles. As electric vehicles become more popular, charging stations have become an important factor for purchasing electric vehicles. Recent research is using artificial intelligence to identify local demand for charging stations and select locations that can maximize economic impact. In this study, in order to contribute to the improvement of the performance of the electric vehicle charging station demand prediction model, nationwide data that can be used in the artificial intelligence model was defined and a pre-processing technique was proposed. In addition, a preprocessor, artificial intelligence model, and service web were implemented for real charging station demand prediction, and the value of data as a location selection factor was verified.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.225-226
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• 2022

In order to secure the practicality of services using drones, improve on-site operability, and improve the convenience of aircraft maintenance, it is necessary to develop a drone station that can be safely stored while ensuring charging performance. Therefore, a total seven points of improvement were derived through laboratory experiments after the development of the first prototype, and an improved second prototype will be developed in the future based on the preceding processes.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1916-1924
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• 2017

Fossil oil, as the main energy of transportation, is destined to be exhausted. The electrification of transportation is a sustainable solution to the energy crisis, since electric power could be acquired from the inexhaustible sun, wind and water. Among all the problems that hinder the development of Electric Vehicle (EV) industry, charging issue might be the most prominent one. In this paper, the service process of a charging station with Battery Energy Storage System (BESS) is analyzed by means of $Cram{\acute{e}}r$ - Lundberg model which has been intensively utilized in ruin theory. The service quality is proposed in two dimensions: the service efficiency and the service reliability. The arrival rate and State of Charge (SOC) upon arrival are derived from 2009 National Household Travel Survey (NHTS). The simulations are performed to show how the service quality is determined by the system parameters such as the number of servers, the service rate, the initial capacity, the charge rate and the maximum waiting time. At last, the economic analysis of the system is conducted and the best combination of the system parameters are given.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.83-90
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• 2020

This study conducted a risk assessment using the HyKoRAM program created by international joint research. Risk assessment was conducted based on accident scenarios and worst-case scenarios that could occur in the facility, reflecting design specifications of major facilities and components such as compressors, storage tanks, and hydrogen pipes in the hydrogen charging station, and environmental conditions around the demonstration complex. By identifying potential risks of hydrogen charging stations, we are going to derive the worst leakage, fire, explosion, and accident scenarios that can occur in hydrogen storage tanks, treatment facilities, storage facilities, and analyze the possibility of accidents and the effects of damage on human bodies and surrounding facilities to review safety.