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A New Methodology for Early Detection of Failures in Lithium-Ion Batteries

Author

Listed:
  • Mario Eduardo Carbonó dela Rosa

    (Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico)

  • Graciela Velasco Herrera

    (Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Mexico City 04510, Mexico)

  • Rocío Nava

    (Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco 3462580, Mexico)

  • Enrique Quiroga González

    (Instituto de Física, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico)

  • Rodolfo Sosa Echeverría

    (Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico)

  • Pablo Sánchez Álvarez

    (Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico)

  • Jaime Gandarilla Ibarra

    (Facultad de Ingeniería, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico)

  • Víctor Manuel Velasco Herrera

    (Instituto de Geofísica, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, Mexico City 04510, Mexico)

Abstract

The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.

Suggested Citation

  • Mario Eduardo Carbonó dela Rosa & Graciela Velasco Herrera & Rocío Nava & Enrique Quiroga González & Rodolfo Sosa Echeverría & Pablo Sánchez Álvarez & Jaime Gandarilla Ibarra & Víctor Manuel Velasco H, 2023. "A New Methodology for Early Detection of Failures in Lithium-Ion Batteries," Energies, MDPI, vol. 16(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1073-:d:1040094
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    References listed on IDEAS

    as
    1. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    2. Schmidt, Marc & Staudt, Philipp & Weinhardt, Christof, 2020. "Evaluating the importance and impact of user behavior on public destination charging of electric vehicles," Applied Energy, Elsevier, vol. 258(C).
    3. Lena Spitthoff & Paul R. Shearing & Odne Stokke Burheim, 2021. "Temperature, Ageing and Thermal Management of Lithium-Ion Batteries," Energies, MDPI, vol. 14(5), pages 1-30, February.
    4. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    5. Ferrero, Enrico & Alessandrini, Stefano & Balanzino, Alessia, 2016. "Impact of the electric vehicles on the air pollution from a highway," Applied Energy, Elsevier, vol. 169(C), pages 450-459.
    6. Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    7. Nicholas Williard & Wei He & Christopher Hendricks & Michael Pecht, 2013. "Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability," Energies, MDPI, vol. 6(9), pages 1-14, September.
    8. Hu, Zhixiong & Prado, Raquel, 2023. "Fast Bayesian inference on spectral analysis of multivariate stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    9. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    10. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
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