• Title/Summary/Keyword: Electric Vehicle Battery

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Analysis of the Effects of Recycling and Reuse of Used Electric Vehicle Batteries in Korea (한국의 전기차 사용 후 배터리 재활용 및 재사용 효과 분석 연구)

  • Yujeong Kim
    • Economic and Environmental Geology
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    • v.57 no.1
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    • pp.83-91
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    • 2024
  • According to the IEA (2022), global rechargeable battery demand is expected to reach 1.3 TWh in 2040. EV batteries will account for about 80% of this demand, and used EV batteries are expected to be discharged after 30 years. Used EV batteries can be recycled and reused to create new value. They can also resolve one of the most vulnerable parts of the battery supply chain: raw material insecurity. In this study, we analyzed the amount of used batteries generated by EV in Korea and their potential for reuse and recycling. As a result, it was estimated that the annual generation of used batteries for EV began to increase to more than 100,000 in '31 and expanded to 810,000 in '45. In addition, it was found that the market for recycling EV batteries in '45 could be expected to be equivalent to the production of 1 million batteries, and the market for reuse could be expected to be equivalent to the production of 36 Gwh of batteries. On the other hand, according to the plan standard disclosed by the recycling company, domestic used EV batteries can account for 11% of the domestic recycling processing capacity (pre-treatment) ('30). So it will be important to manage the import and export of used batteries in terms of securing raw materials.

Study on Selective Lithium Leaching Effect on Roasting Conditions of the Waste Electric Vehicle Cell Powder (폐전기차 셀분말의 열처리 조건에 따른 선택적 리튬침출 연구)

  • Jung, Yeon Jae;Son, Seong Ho;Park, Sung Cheol;Kim, Yong Hwan;Yoo, Bong Young;Lee, Man Seung
    • Resources Recycling
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    • v.28 no.6
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    • pp.79-86
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    • 2019
  • Recently, the use of lithium ion battery(LIB) has increased. As a result, the price of lithium and the amount spent lithium on ion battery has increased. For this reason, research on recycling lithium in waste LIBs has been conducted1). In this study, the effect of roasting for the selective lithium leaching from the spent LIBs is studied. Chemical transformation is required for selective lithium leaching in NCM LiNixCoyMnzO2) of the spent LIBs. The carbon in the waste EV cell powder reacts with the oxygen of the oxide at high temperature. After roasting at 550 ~ 850 ℃ in the Air/N2 atmosphere, the chemical transformation is analysed by XRD. The heat treated powders are leached at a ratio of 1:10 in D.I water for ICP analysis. As a result of XRD analysis, Li2CO3 peak is observed at 700 ℃. After the heat treatment at 850 ℃, a peak of Li2O was confirmed because Li2CO3 is decomposed into Li2O and CO2 over 723 ℃. The produced Li2O reacted with Al at high temperature to form LiAlO2, which does not leach in D.I water, leading to a decrease in lithium leaching ratio. As a result of lithium leaching in water after heat treatment, lithium leaching ratio was the highest after heat treatment at 700 ℃. After the solid-liquid separation, over 45 % of lithium leaching was confirmed by ICP analysis. After evaporation of the leached solution, peak of Li2CO3 was detected by XRD.

A numerical analysis study on the flammable volume by leakage of hydrogen fuel vehicles in parking lot (지하주차장 내 수소연료차의 수소 방출시 가연체적에 관한 수치해석적 연구)

  • Lee, Ho-Hyung;Kim, Hyo-Gyu;Yoo, Ji-Oh;Kim, Doo-Young
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.23 no.6
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    • pp.439-449
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    • 2021
  • The recent reduction in greenhouse gases, interest in environmental pollution such as low-carbon emission policies is increasing. Accordingly, the penetration rate of eco-friendly vehicles, including hydrogen battery vehicles capable of reducing carbon emission, is increasing, and thus it is required for disaster prevention and safety-related measures. In this study, the degree of risk for the concentration distribution of hydrogen when leaking hydrogen fuel vehicles according to ventilation conditions was analyzed through numerical analysis, limited to places in parking lots. As a result, when only one hydrogen tank was released, the combustible volume ratio of hydrogen in the underground parking lot was up to 8.6%, and as ventilation continued, the volume ratio of combustible hydrogen decreased to less than 1% after 150 seconds, indicating that mechanical ventilation is essential. In the case of simultaneous release or stage release of three hydrogen tanks, the final combustible volume ratio of hydrogen is similar, but the increase in the combustible volume ratio of hydrogen in the early stage of release is low, and further research is expected.

Sintering behavior and electrical properties of transition metal (Ni, Co, Mn) based spinel oxides for temperature sensor applications (복합전이금속(Ni, Co, Mn) 기반 스피넬계 산화물의 소결 거동 및 온도센서 특성 연구)

  • Younghee So;Eunseo Lee;Jinyoung Lee;Sungwook Mhin;Bin Lee;Hyung Tae Kim
    • Journal of the Korean Crystal Growth and Crystal Technology
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    • v.34 no.2
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    • pp.73-77
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    • 2024
  • The spinel-type oxide (Nix, Mny, Co3-x-y)O4 (NMC) is widely utilized as a material for temperature sensors with a negative temperature coefficient (NTC), finding applications across various industries including electric vehicle battery management systems. Typically, NMC is manufactured using solid-state reaction methods employing powders of Ni, Mn, and Co compounds, with the densification process through sintering recognized as a crucial factor determining the electrical properties of the temperature sensor material. In this study, NMC pellets were synthesized via solid-state reaction and their crystallographic and microstructural characteristics were investigated. Also, the activation energy for densification behavior during the sintering process was determined. According to the analysis results, the room temperature resistance of the NMC pellets was measured at 10.03 Kohm, with the sensitivity parameter, B-value, recorded at 3601.8 K, indicating their potential applicability as temperature sensors across various industrial fields. Furthermore, the activation energy for densification was found to be 273.3 ± 0.4 kJ/mol, providing valuable insights into the thermodynamic aspects of the sintering process of the NMC.

Development of deep learning network based low-quality image enhancement techniques for improving foreign object detection performance (이물 객체 탐지 성능 개선을 위한 딥러닝 네트워크 기반 저품질 영상 개선 기법 개발)

  • Ki-Yeol Eom;Byeong-Seok Min
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.99-107
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    • 2024
  • Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.