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Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography

Author

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  • Yi Wu

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Saurabh Saxena

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Yinjiao Xing

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Youren Wang

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Chuan Li

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

  • Winco K. C. Yung

    (Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

Abstract

Premature battery drain, swelling and fires/explosions in lithium-ion batteries have caused wide-scale customer concerns, product recalls, and huge financial losses in a wide range of products including smartphones, laptops, e-cigarettes, hoverboards, cars, and commercial aircraft. Most of these problems are caused by defects which are difficult to detect using conventional nondestructive electrical methods and disassembly-based destructive analysis. This paper develops an effective computed tomography (CT)-based nondestructive approach to assess battery quality and identify manufacturing-induced defects and structural deformations in batteries. Several unique case studies from commercial e-cigarette and smartphone applications are presented to show where CT analysis methods work.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:925-:d:140947
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. 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.
    4. Nan Zhou & Xiulong Cui & Changhao Han & Zhou Yang, 2022. "Analysis of Acoustic Characteristics under Battery External Short Circuit Based on Acoustic Emission," Energies, MDPI, vol. 15(5), pages 1-16, February.
    5. Péter Földesi & László T. Kóczy & Ferenc Szauter & Dániel Csikor & Szabolcs Kocsis Szürke, 2022. "Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles," Energies, MDPI, vol. 15(13), pages 1-16, June.
    6. Xu, Bin & Lee, Jinwoo & Kwon, Daeil & Kong, Lingxi & Pecht, Michael, 2021. "Mitigation strategies for Li-ion battery thermal runaway: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    7. Mona Faraji Niri & Jimiama Mafeni Mase & James Marco, 2022. "Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure," Energies, MDPI, vol. 15(12), pages 1-20, June.
    8. 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).
    9. Thomas F. Landinger & Guenter Schwarzberger & Guenter Hofer & Matthias Rose & Andreas Jossen, 2021. "Power Line Communications for Automotive High Voltage Battery Systems: Channel Modeling and Coexistence Study with Battery Monitoring," Energies, MDPI, vol. 14(7), pages 1-26, March.
    10. Bingtao Hu & Yixiong Feng & Hao Zheng & Jianrong Tan, 2018. "Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm," Energies, MDPI, vol. 11(8), pages 1-18, August.

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