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Thermal Modelling Analysis of Spiral Wound Supercapacitor under Constant-Current Cycling

Author

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  • Kai Wang
  • Liwei Li
  • Huaixian Yin
  • Tiezhu Zhang
  • Wubo Wan

Abstract

A three-dimensional modelling approach is used to study the effects of operating and ambient conditions on the thermal behaviour of the spiral wound supercapacitor. The transient temperature distribution during cycling is obtained by using the finite element method with an implicit predictor-multicorrector algorithm. At the constant current of 2A, the results show that the maximum temperature appears in core area. After 5 cycles, the maximum temperature is 34.5°C, while in steady state, it’s up to 42.5°C. This paper further studies the relationship between the maximum temperature and charge-discharge current. The maximum temperature will be more than 60°C after 5 cycles at the current of 4A, and cooling measurements should be taken at that time. It can provide thoughts on inner temperature field distribution and structure design of the spiral wound supercapacitor in working process.

Suggested Citation

  • Kai Wang & Liwei Li & Huaixian Yin & Tiezhu Zhang & Wubo Wan, 2015. "Thermal Modelling Analysis of Spiral Wound Supercapacitor under Constant-Current Cycling," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-11, October.
    • Handle: RePEc:plo:pone00:0138672
    DOI: 10.1371/journal.pone.0138672
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Kai Wang & Wanli Wang & Licheng Wang & Liwei Li, 2020. "An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems," Energies, MDPI, vol. 13(20), pages 1-13, October.
    4. Cui, Shuhui & Lyu, Shouping & Ma, Yongzhi & Wang, Kai, 2024. "Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE," Energy, Elsevier, vol. 307(C).
    5. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.

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