• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.234-242
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• 2016

In express operation plans for urban trains, it is effective for the reduction of the number of sidetracks to apply a high performance train that has improved acceleration/deceleration ability and a regular train to local and express trains, respectively. In this research, based on a plan to use a high performance train for a local train, an optimization methodology is suggested to reduce the number of sidetracks and the operation time of the local train simultaneously. The optimization solver applied in this research is a genetic algorithm; headway, location of sidetrack and waiting time at the sidetrack are considered as design variables in the optimization problem. Consequently, by applying this system to Seoul metro line no.7, the effect of the suggested methodology was verified by obtaining the proper optimum solution.

• Journal of the Korean Society for Railway
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• v.20 no.1
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• pp.11-19
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• 2017

Since operation of railway trains is a major source of particle pollution in tunnel air, a particle removal device can be an effective measure to remove wear particles. To obtain design conditions of the particle removal device that will be installed underneath the railway trains, the wind speed and particle concentration underneath the trains were investigated using a three-dimensional ultrasonic anemometer and a DustTrak aerosol monitor, respectively. The measurements were made for the trains running on Seoul Metropolitan Subway Line 5 on February 10, 2015. The data were analyzed according to the track geometry (straight, curved) and train speed pattern (acceleration, cruising, and deceleration) between stations. Train speed was also analyzed. The average wind speed and $PM_{10}$ concentration underneath the trains were ~30% of the train speed and ${\sim}200{\mu}g/m^3$ for both straight and curved sections. Average $PM_{10}$ concentration for deceleration sections was higher than that for acceleration sections.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.344-351
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• 2016

In the point of view of a train operator, the main concern with a train operation is not only to maintain a time schedule, but also to decrease the energy consumption as much as possible. Generally for a manual drive, a train conductor controls the train acceleration and deceleration by controlling the notches not to exceed the regulation velocity by considering the given maximum velocity profile for an operation route. For this case, the guideline for a conductor is needed to choose the proper notches by applying the notch optimization so as to drive at the regulation velocity and minimize energy consumption simultaneously. In this paper, the real-time notch optimization plan is suggested using a genetic algorithm that optimizes the notches for the remaining route in real time when the event occurs that track information or regulation velocity profile of the remaining route changes during train operation as well as a normal operation situation. An energy saving effect and the convergence behavior of the optimal solution obtained was analyzed in a genetic algorithm.

• Proceedings of the KSR Conference
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• 2000.11a
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• pp.666-673
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• 2000

This paper describes the full load test results of IGBT VVVF inverter for the railway propulsion system. The 1,650kVA IGBT VVVF inverter has been developed. Therefore, the field test is performed in SMG 6 Line to confirm its the reliability and performance. The train consists of 4M4T(4 Motor car 4 Trailer Car) and the electrical equipment for field test are as follows VVVF inverter 4 sets, 16 traction motors and 2 SIVs. The propulsion system is composed with IC4M(1-Controller 4-Motors). The results of propulsion system which have the excellent acceleration/deceleration and the jerk characteristics as well as starting ability on slope are taken through the field test.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.3
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• pp.310-317
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• 2014

The Next-generation High-speed Rail Technology Development Project was started in 2007 by the Korean Government with the aim of developing the core technologies for a high-speed electric multiple unit (EMU) railway system. This is the first attempt to develop a high-speed EMU railway. High-speed EMU trains have superior acceleration and deceleration compared to push-pull high-speed railways such as KTX(Korean Train eXpress). A prototype train was developed and tested on a high-speed line starting in 2012. The new train must maintain running safety during the test. Generally, the international standard (UIC518) is adopted to evaluate the running safety of trains. This method suggests that the test zone must have over 25 sections, and the length of each section must be 500 m. However, it is difficult to implement these test conditions for a real high-speed line. In this study, we analyzed the running safety using several test section lengths (100 m to 500 m) and compared the results. The results of this study will be used to establish a running safety evaluation method for high-speed EMU railways.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.402-409
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• 2013

In metropolitan railway systems, the average commuting time keeps increasing as the scheduled speed increases, and this leads to a decline of rail service usage and competitiveness. Therefore, effective express operation for urban trains is required to improve the scheduled speed. In this study, based on the obtainable time shortening efficiency and economic viability, several express operations are suggested for urban railways and these suggestions are compared by considering high performance trains with acceleration/deceleration and maximum speed improvement. As a result, the optimum express system, which can minimize the cost for sidetrack construction, is suggested.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.498-505
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• 2010

In-Wheel System is a high-efficiency system to supply a new concept of platform which raises the efficiency of motor drive system and applies it to an environment-friendly automobile by installing a highly efficient electric motor directly to wheels and removing factors of power train. The proliferation of these systems is directly related to the safety of our lives, so check the reliability of the part in the development phase and should be certified. Reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time. This paper presents to the verification methods for durability, one of reliability assessments of the Motor, the study calculated acceleration and deceleration torque and the effective torque from driving conditions of In-Wheel Motor, and based on this, it reduced the test time and suggested the verification methods of In-Wheel Motor reliability through the accelerated life test.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.