• Title/Summary/Keyword: 차량대기길이

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Improved Drying Process for Electrodes in Production of Lithium-Ion Batteries for Electric Vehicles (전기자동차용 리튬이온 전지의 제조공정을 위해 개선된 극판 건조 기술)

  • Jang, Chan-Hee;Lee, Jae-Chon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.6
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    • pp.37-45
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    • 2018
  • An electric vehicle is an environmentally friendly vehicle because there is no exhaust gas, unlike gasoline automobiles. On the other hand, because the electric vehicle is driven by electric power charged in batteries, the distance to go through a single charge depends on the energy density of the batteries. Therefore, a lithium-ion battery with a high energy density is a good candidate for batteries in electric vehicles. Because the electrode is an essential component that governs the efficiency of a lithium-ion battery, the electrode manufacturing process plays a vital role in the entire production process of lithium-ion batteries. In particular, the drying process during the electrode manufacturing process is a critical process that has a significant influence on the performance. This paper proposes an innovative process for improving the efficiency and productivity of the drying process in electrode manufacturing and describe the equipment design method and development results. In particular, the design procedure and development method for enhancing the electrode adhesion power, atmospheric pressure superheated steam drying technology, and drying furnace slimming technologies are presented. As a result, high-speed drying technology was developed for battery electrodes through the world's first turbo dryer technology for mass production using open/integrated atmospheric pressure superheated steam. Compared to the conventional drying process, the drying furnace improved the productivity (Dry Lead Time $0.7min{\rightarrow}0.5min$).

Study on Influencing Factors of Traffic Accidents in Urban Tunnel Using Quantification Theory (In Busan Metropolitan City) (수량화 이론을 이용한 도시부 터널 내 교통사고 영향요인에 관한 연구 - 부산광역시를 중심으로 -)

  • Lim, Chang Sik;Choi, Yang Won
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.35 no.1
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    • pp.173-185
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    • 2015
  • This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.