• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.211-224
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• 2022

In this study, factors affecting the efficacy of EV charging infrastructure improvement were investigated for EV users on Jeju Island. This study analyzed satisfaction with the EV charging infrastructure and demographic factors that affect the efficacy of EV charging infrastructure improvement. Factors found to affect the efficacy of EV charging infrastructure improvement include a sufficient number of charger installations, the speed in using EV chargers, the ease of obtaining additional information about charging, and fast customer service for faulty chargers. It was also confirmed that demographic factors such as user's housing types had a significant effect. This study contributes to verifying user satisfaction with the construction of EV charging infrastructure throughout Jeju Island.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-10
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• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.205-214
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• 2013

In this paper, we analyzed the various EV charging-infra information(charging status, charging pattern, charging rate, charging fee, etc.) through the charging infra simulator which would be of help to effectively construct the EV charging infrastructure. The proposed simulator virtually made the EV motoring pattern referred to TMS(Traffic Monitoring System) & Ministry of Land, Transport and Maritime Affairs, and analyzed the charging-infra information(amount of charging, accumulated charging fee, etc.) based on vehicle types, charging type, time and days using EV charging-fee list noticed by KEPCO. Through this simulator, we deducted some considerable contents to build the EV charging infrastructure similarly with real environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1138-1143
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• 2013

In this paper, we propose a smartphone application based on a wireless fidelity(WiFi) in order to control the charging of electric vehicle(EV) and monitor the charging status together with the vehicle history information. The driver obtains much information on vehicle status through a smartphone application which communicates with the electric vehicle supply equipment(EVSE) management server while the EV also communicates with the EVSE for the authentification through controller area network(CAN). We also implement the simulator for the EV charging control system to verify the functions of the proposed application where the simulator consists of an EV model, an EVSE, and a smartphone. It is shown by the simulator that the proposed smartphone application allows the driver to control and to monitor the charging process of an EV conveniently and, moreover, it can provide the driver with vehicle information stored in the EVSE management server.

• 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.1
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• pp.451-458
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• 2017

An Electric Vehicle (EV) is operated with the electric energy of a battery in place of conventional fossil fuels. Thus, a suitable charging infrastructure must be provided to expand the use of electric vehicles. Because the battery of an EV must be charged to operate the EV, expanding the number of EVs will have a significant influence on the power supply and demand. Therefore, to maintain the balance of power supply and demand, it is important to be able to predict the numbers of charging EVs and monitor the events that occur in the distribution system. In this paper, we predict the hourly charging rate of electric vehicles using transformation matrix, which can describe all behaviors such as resting, charging, and driving of the EVs. Simulation with transformation matrix in a specific region provides statistical results using the Monte-Carlo Method.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.937-944
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• 2022

Electric vehicles (EVs) have been growing to reduce energy consumption and greenhouse gas (GHG) emissions in the transportation sector. The increasing number of EVs requires adequate recharging infrastructure, and at the same time, adopts low- or zero-emission electricity production because the GHG emissions are highly dependent on primary sources of electricity production. Although previous research has studied solar photovoltaic (PV) -integrated EV charging stations, it is challenging to optimize spatial areas between where the charging stations are required and where the renewable energy sources (i.e., solar photovoltaic (PV)) are accessible. Therefore, the primary objective of this research is to support decisions of siting EV charging stations using a spatial data clustering method integrated with Geographic Information System (GIS). This research explores spatial relationships of PV power outputs (i.e., supply) and traffic flow (i.e., demand) and tests a community in the state of Indiana, USA for optimal sitting of EV charging stations. Under the assumption that EV charging stations should be placed where the potential electricity production and traffic flow are high to match supply and demand, this research identified three areas for installing EV charging stations powered by rooftop PV in the study area. The proposed strategies will drive the transition of existing energy infrastructure into decentralized power systems. This research will ultimately contribute to enhancing economic efficiency and environmental sustainability by enabling significant reductions in electricity distribution loss and GHG emissions driven by transportation energy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.57-69
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• 2022

Even though demand to charge EV(electric vehicles) is increasing, there are some problems to construct EV charging stations and problems from deficient them. Typical problem of EV charging stations is discordance for EV charging station location with its demand. This study investigates methods to determine the optimized location for fast EV charging stations considering charging demand in Seoul. Firstly, variables influencing on determination of determine the optimized location for fast EV charging stations were decided, and then evaluation of weights of the variables and data collection were conducted. Using the weights, location potential scores for each area-cell were calculated and optimized locations for fast EV charging stations were resulted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.137-167
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• 2015

Based on the stakeholder theory, this paper analyzes a clash of standards in Korea's Electric Vehicle(EV) market, particularly between an EV charging standard and a smart grid communication standard in 2012~2013. For charging, EV is connected with the electric power grid and simultaneously exchanges data regarding the charging status. When EV is connected with the power grid, a clash between two standards may arise. It actually happened when BMW entered into the Korean EV market with the DC Combo charging system. In that course, the frequency interference occurred between the EV data communication technology adopted by BMW and the AMI(Advanced Metering Infrastructure) for the smart grid system in Korea. Standardization of Korea's EV charging systems was required to solve this problem. However, it had been delayed due to the confrontation between various stakeholders involved in the process of standardization. It lasted until the DC combo was accepted as one of the Korea EV charging standards(KSAE SAE 1772-2040, 2014.1) by KSAE(The Korea Society of Automotive Engineers) in January 2014. This is an interesting case in the age of convergence. As it deals with the standard competition not among EV standards, but a clash between the EV industry and the smart grid, i.e. electric power industry, it addresses the necessity to consider standardization processes between different industries. This study draws on the stakeholder theory to analyse the dynamics of the standard clash between EV charging systems and the smart grid system, which is a unique example of standard clash between different industries. We expect such clashes to increase in the age of convergence.