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State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning

Author

Listed:
  • Xiaoyu Li

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Chuxin Wu

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Chen Fu

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Shanpu Zheng

    (Institute of Structural Health Management, Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China)

  • Jindong Tian

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
    Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China)

Abstract

Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries based on ultrasonic guided wave (UGW) scanning is carried out. The laser Doppler vibrometer (LDV) and the X-Y stage are used to obtain the surface scanning UGW signal and the line scanning UGW signal of lithium-ion batteries under different states of charge and different aging degrees. The propagation law of UGWs in the battery is analyzed by surface scanning signals, then the energy spectrum of the signals is calculated, showing that the aging of the battery attenuates the transmission energy of UGWs. The “point” parameters are extracted from the scanning point signals. On this basis, the “line” parameters composed of line scanning multi-point signals are extracted. By analyzing the changing law of parameters during the charge–discharge process of batteries, several characteristic parameters that can be used to characterize the battery state of charge and state of health are obtained. The method has good consistency in the state characterization of the three batteries and provides a new approach for non-destructive testing and evaluation of battery states.

Suggested Citation

  • Xiaoyu Li & Chuxin Wu & Chen Fu & Shanpu Zheng & Jindong Tian, 2022. "State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning," Energies, MDPI, vol. 15(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6027-:d:892999
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    References listed on IDEAS

    as
    1. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    2. Peter Kurzweil & Wolfgang Scheuerpflug & Bernhard Frenzel & Christian Schell & Josef Schottenbauer, 2022. "Differential Capacity as a Tool for SOC and SOH Estimation of Lithium Ion Batteries Using Charge/Discharge Curves, Cyclic Voltammetry, Impedance Spectroscopy, and Heat Events: A Tutorial," Energies, MDPI, vol. 15(13), pages 1-21, June.
    3. Christophe Savard & Emiliia Iakovleva & Daniil Ivanchenko & Anton Rassõlkin, 2021. "Accessible Battery Model with Aging Dependency," Energies, MDPI, vol. 14(12), pages 1-16, June.
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    Cited by:

    1. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    2. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.

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