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Study on Lifetime Decline Prediction of Lithium-Ion Capacitors

Author

Listed:
  • Shuhui Cui

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    Shandong Suoxiang Intelligent Technology Co., Ltd., Weifang 261101, China)

  • Saleem Riaz

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    Shandong Suoxiang Intelligent Technology Co., Ltd., Weifang 261101, China)

Abstract

With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction techniques, however, continue to be active research areas. This paper examines the aging process for Li-ion batteries, covering the alterations in cell composition, the effect of the electrode charge state, temperature effects, and electrolyte deterioration. Additionally, this research offers approaches for predicting the lifespan of lithium-ion batteries, including those based on physical models, machine learning, and artificial intelligence. In this work, cycle life testing techniques are also discussed, including accelerated aging experiments for lithium-ion capacitors. The paper concludes by discussing future directions for the creation of aging mechanisms and lithium-ion capacitor life prediction techniques.

Suggested Citation

  • Shuhui Cui & Saleem Riaz & Kai Wang, 2023. "Study on Lifetime Decline Prediction of Lithium-Ion Capacitors," Energies, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7557-:d:1279320
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    References listed on IDEAS

    as
    1. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    2. Maosong Fan & Mengmeng Geng & Kai Yang & Mingjie Zhang & Hao Liu, 2023. "State of Health Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(8), pages 1-14, April.
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    4. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    5. Wang, Chenxu & Xiong, Rui & Tian, Jinpeng & Lu, Jiahuan & Zhang, Chengming, 2022. "Rapid ultracapacitor life prediction with a convolutional neural network," Applied Energy, Elsevier, vol. 305(C).
    6. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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