• 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.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.601-611
• /
• 2017

This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model-based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.5
• /
• pp.1027-1037
• /
• 2012

With response to the critical issue of global warming, Smart Grid system has been extensively investigated as next efficient power grid system. Domestically, Korean is trying to expand the usage of Electric Vehicles (EVs) and the charging infrastructure in order to replace the current transportation using fossil fuels holding 20% of overall CO2 emission. The EVs charging infrastructures are combined with IT technologies to build intelligent environments but have considerable number of cyber security issues because of its inherent nature of the technologies. This work not only provides logical architecture of EV charging infrastructures with security threats based on them but also analyses security requirements against security threats in order to overcome the adversarial activities to Smart Grid.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.39 no.4
• /
• pp.371-378
• /
• 2015

Range extenders, which are power generation systems driven by small engines, extend the driving distance and time of electric vehicles (EVs) through continuous charging of batteries. The currently used range extenders with gasoline engines pose limitations with regard to the realization of high-power compact systems, owing to their complex structure and low energy density. In contrast, micro gas turbine (MGT) range extenders (MGT power packs) possess high power and low weight, and can therefore be significantly reduced in size despite increase in speed. In this study, an MGT power pack for the range extenders of EVs was developed using a turbo-prop micro turbine, an alternator for passenger vehicles and electric batteries. The operating characteristics of the MGT power pack were measured through a series of experiments conducted under electrical no-load and load conditions. Their power generation performance and efficiency were measured under various electrical loads and power transmission gear ratios. From the results, electrical load was found to have no influence on power generation performance. The maximum electrical power output was 0.8 kW at a core turbine speed of 150 krpm, and the application of 3:1 reduction gear to the turbine output shaft increased the power to 1.5 kW by 88%. This implies that the test results demonstrated stable power generation performance of the MGT power pack regardless of vehicle load changes, thus revealing its feasibility for use with the range extenders of EVs.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.12
• /
• pp.762-767
• /
• 2017

This paper presents an analysis for improving the power transfer capability in transmission lines caused by the movable load charging of electric vehicles (EVs). EVs are expected to be used more widely and replace gas fuel vehicles in the near future due to the shortage of fossil fuels and for environmental preservation. Movable load charging of EVs could lead to the convergence of transferred power flow and overloading conditions in transmission lines in a specific area of a power system, which is conventionally based on estimated fixed load capability. To analyze these conditions, the New England Test System was divided into four regions based on the load characteristics, and different charging scenarios were considered. In these scenarios, the regional power load was highly increased to 31% based on the standard charging capacity of an EV. As a solution to the overloading problem of transmission lines, a TCSC was installed serially on the overloaded line to directly control the transferred power under limited line capability (100% load capability). The simulation showed that the application of a few TCSCs could efficiently and economically control the line capability problem caused by movable load charging of EVs.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.5
• /
• pp.199-208
• /
• 2017

Lithium polymer batteries are used in energy storage systems (ESS), electric vehicles (EVs), etc. due to their high safety, fast charging and long lifecycle, and now they are used in agricultural drones. However, when overcharging and overdischarging, the lithium-polymer battery is destroyed in the gap structure in the lithium-ion battery and the battery life is reduced. In order to prevent overcharge and overdischarge, uneven cell voltage Cell balancing system is needed. In this paper, a fuzzy controller suitable for nonlinear systems is proposed by detecting the unbalanced cells by detecting the voltage difference between charging and discharging of each cell, and suggesting the applied cell balancing algorithm. In this paper, we have designed the cell balancing of the battery pack of agricultural drones by fuzzy control and it is designed for equal control between cells. As a final result, we checked whether cell balancing is good, and when there are two cells, Cell balancing was confirmed. We tested whether it could be used for other products. As a result, we confirmed that cell balancing is good regardless of the number of cells used.

• Journal of Energy Engineering
• /
• v.20 no.2
• /
• pp.109-122
• /
• 2011

The production capacity of EV models should be sufficient to achieve the goal of one million EVs by 2015. Large-Format lithium-ion battery are expected to find a prominent role as ideal electrochemical storage systems in traction power train for sustainable vehicles such as all-electric vehicles. This review focuses first on the present status of production lithium-ion battery technology and cooperative relations of between battery and EV makers, then on its near future development.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.5
• /
• pp.106-114
• /
• 2014

The interior permanent-magnet synchronous motor (IPMSM) is often used for the traction motor of hybrid electric vehicles (HEVs) and electric vehicles (EVs) due to its high power density and wide speed range. This paper introduces the 120kW class IPMSM for traction motors in military trucks. This system, as a SHEV (series hybrid electric vehicle), requires a traction motor that can generate high torque. This study introduces a way to reduce costs by proposing a design approach that creates reluctance torque that can be maximized by varying the dq-axis inductance. If a model designed by a design approach meets the desired torque, the magnetic torque can be reduced by an amount equal to the increase in reluctance torque and consequently the amount of permanent magnets can be reduced. A reduction gear and high speed operation of motors are necessary for the miniaturization of the motor. Thus, a fairly large centrifugal force is generated due to the high speed rotation. This force causes mechanical interference between the rotor and the stator, and a design approach for adding an iron bridge is explained to solve the interference. In this study, the initial model and the improved model that reduces cost and improves mechanical durability are compared by FEA, and the models are produced. Finally, the FEM results were verified experimentally.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.171-177
• /
• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.1
• /
• pp.1-10
• /
• 2013

The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.