• International Journal of Internet, Broadcasting and Communication
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• 제9권2호
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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.

• 전기학회논문지
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• 제62권8호
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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 Electrical Engineering and Technology
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• 제12권1호
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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.

• 한국산업융합학회 논문집
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• 제24권6_1호
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• pp.669-681
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• 2021

As the supply of xEV in Korea is rapidly increasing, the amount of waste batteries is expected to increase rapidly, but the current recycling system for waste xEV batteries is very insufficient. In order to properly utilize the xEV reusable battery module, it is essential to classify it into a type that has similar discharge characteristics to the current state of health(SOH), which is the discharge capacity of the battery. This paper proposes a system that can minimize the exchange of energy with the KEPCO system by using the charging/discharging method by circulating power between batteries in order to minimize the power consumption when charging and discharging waste batteries. In the proposed system, a function to measure parameters during the charging/discharging test of the waste battery was implemented to build a customized big date for the test waste battery. In addition, the dynamic characteristics of the proposed circuit were analyzed using PSIM, which is useful for power electronics analysis, and the validity of the proposed circuit was verified through experiments.

• 기술혁신연구
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• 제23권3호
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• pp.137-167
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• 2015

본 연구는 2012~2013년 국내 전기차 시장에서 크게 논란이 되었던 콤보 전기 자동차 충전 표준과 국내 스마트그리드 표준 간 충돌 사례를 이해관계자 이론을 활용하여 분석하였다. 사례 연구를 위해 문헌 조사 및 복수의 전문가 인터뷰를 실시하였다. 전기 자동차는 충전을 위해 전력망과 연결될 때 데이터 교환을 한다. 따라서 통신 표준 문제가 대두된다. BMW는 글로벌 전기 자동차 시장에서 업계를 대표하는 사실상 표준의 지위를 얻어 가고 있던 콤보 충전 기술을 채택하여 국내 전기차 시장에 진입하였는데, 그 과정에서 국내 스마트 그리드 전력망의 원격검칭용 통신 기술과 상호 주파수 간섭을 일으키는 문제가 발생하였다. 이 문제를 둘러싸고 이해관계자들의 대립이 계속되면서 국내 전기 자동차 충전 기술 관련 표준(국가표준 및 단체표준)화를 위한 의사결정들이 지연되었고, 이 논란은 2014년 1월 콤보 기술이 한국자동차공학회의 단체표준(KSAE SAE 1772-2040, 2014.1)으로 인정되면서 일단락되었다. 이 사건은 전기 자동차 표준 그 자체만이 아니라, 융합산업 시대에 있어서 타 산업 표준과의 충돌 및 조정의 필요성을 부각시키는 의미있는 사례이다. 본 연구는 전기 자동차 충전 표준과 스마트그리드 표준이라는 이종 산업 간의 표준 충돌의 복잡한 역학 관계를 이해 관계자 이론을 적용하여 분석하였다. 본 연구는 표준화 과정을 둘러싼 경험적 및 이론적 연구가 충분하지 않다는 점에서, 이해관계자 이론을 활용한 사례 축적과 방법론 정립에 기여하고, 또한 표준화 과정에 참가하는 이해관계자들이 이 과정에 영향을 미치기 위한 행위 양식 정립과 전략 수립에 유용한 시사점을 제공한다.

• 전자공학회논문지
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• 제50권1호
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• pp.205-214
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• 2013

본 논문에서는 전기자동차 충전인프라를 효율적으로 보급하는데 도움이 되도록 가상 충전인프라 시뮬레이터를 구현하여 다양한 전기자동차 충전인프라 정보들(충전현황, 충전패턴, 충전부하량, 충전요금 등)를 생성하고 그 결과를 분석하였다. 제안하는 시뮬레이터는 교통량정보제공시스템과 국토해양부 등의 통계자료를 바탕으로 전기자동차의 운행특성을 모의하였으며, 그에 따른 충전부하량 및 충전패턴을 분석할 수 있도록 구현되었다. 또한, 한국전력공사(KEPCO)에서 고시한 전기자동차 충전요금(안)을 적용하여 충전유형별, 차량용도별 및 시간대별 충전요금정산 결과를 분석해 볼 수 있었다. 본 논문에서는 제안하는 전기자동차 충전인프라 시뮬레이터를 통하여 전기자동차 충전인프라를 구성할 때 고려해야하는 요소들을 현실적인 상황과 유사하게 모의하여 분석할 수 있었다.

• 전력전자학회논문지
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• 제6권6호
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• pp.467-473
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• 2001

전기자동차 시스템에서 전지관리장치는 배터리의 접압과 온도, 충방전 전류를 검지하고 관리하여, 전기자동차의 운정상태에 따라 충전상태 (SOC)를 추정하여 배터리를 최적 관리하는 역할을 본다. 본 논문에서는 BMS의 역할과 기능에 대한 적합한 알고리즘을 제시하고 이를 EV 차량에 탑재 적용하여 주행시험 및 성능 시험을 행하여 타당성을 입증하였다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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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 Electrical Engineering and Technology
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• 제13권2호
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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.