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Prediction of the Remaining Useful Life of Supercapacitors

Author

Listed:
  • Zhenxiao Yi
  • Kun Zhao
  • Jianrui Sun
  • Licheng Wang
  • Kai Wang
  • Yongzhi Ma
  • Ali Ahmadian

Abstract

As a new type of energy-storage device, supercapacitors are widely used in various energy storage fields because of their advantages such as fast charging and discharging, high power density, wide operating temperature range, and long cycle life. However, the degradation and failure of supercapacitors in large-scale applications will adversely affect the operation of the whole system. To maximize the efficiency of supercapacitors without damaging the equipment and to ensure timely replacement before reaching the end of their useful life, it is critical to accurately predict the remaining useful life of supercapacitors. This paper presents a comprehensive review of model-based and data-driven approaches to predict the remaining useful life of supercapacitors, introduces the characteristics of the various methods, and foresees future trends, with the expectation of providing a reference for further research in this field.

Suggested Citation

  • Zhenxiao Yi & Kun Zhao & Jianrui Sun & Licheng Wang & Kai Wang & Yongzhi Ma & Ali Ahmadian, 2022. "Prediction of the Remaining Useful Life of Supercapacitors," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, May.
    • Handle: RePEc:hin:jnlmpe:7620382
    DOI: 10.1155/2022/7620382
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    Citations

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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. Shengyang Lu & Yu Zhu & Lihu Dong & Guangyu Na & Yan Hao & Guanfeng Zhang & Wuyang Zhang & Shanshan Cheng & Junyou Yang & Yuqiu Sui, 2022. "Small-Signal Stability Research of Grid-Connected Virtual Synchronous Generators," Energies, MDPI, vol. 15(19), pages 1-17, September.
    3. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    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. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    6. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.

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