• Proceedings of the KSR Conference
• /
• 2010.06a
• /
• pp.1855-1860
• /
• 2010

Electric vehicles have reached a new level of development with introductions by Chrysler, Ford, Honda and Toyota. Today's charging technology includes conductive and inductive charging systems. There are three standardized charging levels: Level 1: charging can be done from a standard, grounded AC 120V, 3-prong outlet available in all homes; Level 2: charging is at AC 240V, 40 amp charging station with special consumer features to make it easy and convenient to plug in and charge EVs at home or at an EV charging station; Level 3: a high-powered charging "fast charge" technology currently under development that will provide a charge in less than 15 minutes. The incoming AC power is converted to DC and stored in the vehicle's batteries. In this paper, we investigated the application of urban railway DC electric power for electric car charging system.

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

• Journal of Climate Change Research
• /
• v.8 no.3
• /
• pp.265-273
• /
• 2017

Recently the Paris Climate Change Accord has been officially put into effect, making global efforts to implement Greenhouse Gas (GHG) reductions, and also International environmental regulations in the automotive sector will be further strengthened. The electric vehicle, which minimizes the particulate matter generated by existing internal combustion engine automobiles, is evaluated as a representative eco-friendly automobile. However, charging the battery of an electric vehicle is not fully environment-friendly if it is fueled by electricity that is being generated by fossil fuels as an energy source. The energy generated by the photovoltaic power generation system, which is an infinite clean energy, can be used to charge an electric vehicle's battery. Currently, shortage of charging facilities, time of charging, and high battery prices are the problem of activating the supply of electric vehicles. This study is to build a conjunction between the EVBSS (Electric Vehicle Battery Supply System) and ESS (Energy Storage System), which can quickly supply the photovoltaic charged battery to the required demand. If the charged battery in the Battery Swapping Station (BSS) is swapped swiftly, it will dramatically shorten the waiting time for charging the battery. As a result, if the battery is rented when it is needed, electric vehicles can be sold without the cost of a battery, which accounts for a large portion of the total cost, then the supply of electric vehicles are expected to expand. Furthermore, it will be an important alternative to maneuver climate change by minimizing GHG emissions from internal combustion engine vehicles.

• KIEAE Journal
• /
• v.14 no.4
• /
• pp.27-33
• /
• 2014

This study analyzed the electric vehicle recharging station status with recharging time limitation due to long waiting time, and figured out the user status by user interviews. And then this study verified the validity of hypothesis in terms of environmental design perspective and suggested layout of recharging station model. 21 recharging stations in Korea and station operation cases of 7 countries were examined. Except for the USA, the reality of electric vehicle recharging station today is the 1st proving stage focusing on the infrastructure construction of electric vehicle recharging station. It focuses on performance of recharging facility, use efficiency and operation environment of electric vehicle. About the effective waiting time of the user to use it should be studied. The current conditions of recharging station are as follows: Lack of independent recharging space, lack of facility that reduces external effect of recharging space, and lack of lounge for users during the waiting time. These three are essential factors constructing a suggesting model after basic layout, which needs proper measurement on the long recharging time and long waiting time. The essential factors are applied to electric vehicle recharging station layout so that users might use 'digital refresh" i.e. lounge and information contents service during the waiting time which provides convenience of recharging and emotional space with users. Such upgrade recharging station environmental model might resolve the burden of long recharging time which may contribute to the popularization of electric vehicles.

• Proceedings of the KIPE Conference
• /
• 2017.07a
• /
• pp.64-65
• /
• 2017

This paper review the core technical trends of TESAL EV(Electric Vehicle) Motors. The object of this study analyzes electric vehicle's body appearance, motor cooling system, battery arrangement, battery management system (BMS), and super charging station etc.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.119-127
• /
• 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
• /
• v.11 no.4
• /
• pp.820-828
• /
• 2016

The sizable electrical load of plug-in electric vehicles may cause a severe low-voltage problem in a distribution network. The voltage drop in a distribution network can be mitigated by limiting the power consumption of a charging station. Then, the charging station operator needs a method for appropriately distributing the restricted power to all plug-in electric vehicles. The existing approaches have practical limitation in terms of the availability of future information and the execution time. Therefore, this study suggests a heuristic method based on priority indexes for fairly distributing the constrained power to all plug-in electric vehicles. In the proposed method, PEVs are ranked using the priority index, which is determined in real time, such that a near-optimal solution can be obtained within a short computation time. Simulations demonstrate that the proposed method is effective in implementation, although its performance is slightly worse than that of the optimal case.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.1916-1924
• /
• 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 Korean Society of Industrial and Systems Engineering
• /
• v.43 no.3
• /
• pp.53-60
• /
• 2020

Evaluating the operational efficiency of electric vehicle charging stations (EVCSs) is important to understand charging network evolution and the charging behavior of electric vehicle users. However, aggregation of efficiency performance metrics poses a significant challenge to practitioners and researchers. In general, the operational efficiency of EVCSs can be measured as a complicated function of various factors with multiple criteria. Such a complex aspect of managing EVCSs becomes one of the challenging issues to measure their operational efficiency. Considering the difficulty in the efficiency measurement, this paper suggests a way to measure the operational efficiency of EVCSs based on data envelopment analysis (DEA). The DEA model is formulated as constant returns of output-oriented model with five types of inputs, four of them are the numbers of floating population and nearby charging stations, distance of nearby charging stations and traffic volume as desirable inputs and the other is the traffic speed in congestion as undesirable one. Meanwhile, the output is given by the charging frequency of EVCSs in a day. Using real-world data obtained from reliable sources, we suggest operational efficiencies of EVCSs in Seoul and discuss implications on the development of electric vehicle charging network. The result of efficiency measurement shows that most of EVCSs in Seoul are inefficient, while some districts (Nowon-gu, Dongdaemun-gu, Dongjak-gu, Songpa-gu, Guro-gu) have relatively more efficient EVCSs than the others.

• Current Photovoltaic Research
• /
• v.11 no.4
• /
• pp.124-133
• /
• 2023

In order to deploy the large-scale energy storage (ES) service in the various industry, it is very important to develop a business model with high technological and economic feasibility through detailed valuation of cost and expected benefits. In relation to this, this paper established an optimal scheduling plan for electric vehicle charging stations connected with photovoltaic (PV) and ES technologies in Korea using the distributed energy resource valuation tool and analyzed the feasibility of the project. In addition, the impact of incentives such as REC (Renewable Energy Certificate) to be given to electric vehicle charging stations in accordance with the relevant laws to be revised in the future was analyzed. As a results, the methodology presented in this paper are expected to be used in various ways to analyze the feasibility of various business models linked to renewable energy and ES technologies as well as the electric vehicle market.