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Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging

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Listed:
  • Daniel Evans

    (Research Institute for Automotive Electronics (E-LAB), HELLA GmbH und Co. KGaA, Beckumer Str. 130, D-59555 Lippstadt, Germany)

  • Paul-Martin Luc

    (Electrical Energy Storage Technology, Department of Energy and Automation Technology, Faculty IV, Secr. EMH 2, Technische Universität Berlin, Einsteinufer 11, D-10587 Berlin, Germany)

  • Claas Tebruegge

    (Research Institute for Automotive Electronics (E-LAB), HELLA GmbH und Co. KGaA, Beckumer Str. 130, D-59555 Lippstadt, Germany)

  • Julia Kowal

    (Electrical Energy Storage Technology, Department of Energy and Automation Technology, Faculty IV, Secr. EMH 2, Technische Universität Berlin, Einsteinufer 11, D-10587 Berlin, Germany)

Abstract

Realising an ideal lithium-ion battery (LIB) cell characterised by entirely homogeneous physical properties poses a significant, if not an impossible, challenge in LIB production. Even the slightest deviation in a process parameter in its production leads to inhomogeneities and causes a deviation in performance parameters of LIBs within the same batch. The greater the number and/or intensity of inhomogeneities, the more they need to be avoided. Severe inhomogeneities (defects), such as metal particle contamination, significantly impact the cell’s performance. Besides electrical measurements, image-based measurement methods can be used to identify defects and, thus, ensure the production quality and safety of LIBs. While the applicability of computed tomography (CT) as an image-based measurement method for detecting defects has been proven, the limitations of this method still need to be determined. In this study, a systematic analysis of the capabilities of CT imaging was conducted. A multilayer pouch cell without an electrolyte was reassembled with several defects on one of the middle anodes. To investigate the boundaries of CT, defects such as a partial and complete removal of the coating, a cut, or a kink, as well as particle contaminations of various sizes and materials (aluminium, copper, iron) were chosen. By comparing the CT images of the cell using laser scanning microscope images of the defective anode, it could be proven that all selected defects except the kink were detectable.

Suggested Citation

  • Daniel Evans & Paul-Martin Luc & Claas Tebruegge & Julia Kowal, 2023. "Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging," Energies, MDPI, vol. 16(19), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6958-:d:1253988
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    References listed on IDEAS

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    1. David, Lamuel & Ruther, Rose E. & Mohanty, Debasish & Meyer, Harry M. & Sheng, Yangping & Kalnaus, Sergiy & Daniel, Claus & Wood, David L., 2018. "Identifying degradation mechanisms in lithium-ion batteries with coating defects at the cathode," Applied Energy, Elsevier, vol. 231(C), pages 446-455.
    2. Pan, Yue & Kong, Xiangdong & Yuan, Yuebo & Sun, Yukun & Han, Xuebing & Yang, Hongxin & Zhang, Jianbiao & Liu, Xiaoan & Gao, Panlong & Li, Yihui & Lu, Languang & Ouyang, Minggao, 2023. "Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses," Energy, Elsevier, vol. 262(PB).
    3. Yi Wu & Saurabh Saxena & Yinjiao Xing & Youren Wang & Chuan Li & Winco K. C. Yung & Michael Pecht, 2018. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography," Energies, MDPI, vol. 11(4), pages 1-22, April.
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