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State-of-charge estimation and remaining useful life prediction of supercapacitors

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  • Liu, Chunli
  • Li, Qiang
  • Wang, Kai

Abstract

As a new type of energy storage device, supercapacitors are widely applied in various fields owing to their irreplaceable extraordinary characteristics. The remaining useful life represents the safe service range of a supercapacitor. Precise monitoring of its value can ensure timely replacement before reaching the service limit. An accurate state-of-charge estimation can ensure that the supercapacitor works in a safe area. Superior precision ensures that the safe area is more explicit. Thus, the supercapacitor can exercise maximum effectiveness without damaging the device. Hence, this paper reviews the above sections. The remaining useful life prediction and state-of-charge estimation of supercapacitors are reviewed based on the model and data. The methods of different innovation points are enumerated, the disparate evaluation frameworks are compared, and their merits and demerits are generalized and reviewed. In the research field, while studying the remaining useful life of supercapacitors based on data, the application of artificial neural networks is emphasized. Hence, this study focuses on reviewing the relevant content for this approach. Finally, the challenges and prospects for the prediction of the above studies are briefly described.

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  • Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:rensus:v:150:y:2021:i:c:s1364032121006924
    DOI: 10.1016/j.rser.2021.111408
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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. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    4. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    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. Chiara Dall’Armi & Davide Pivetta & Rodolfo Taccani, 2023. "Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects," Energies, MDPI, vol. 16(4), pages 1-34, February.
    7. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    8. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    9. Dong, Ao & Ma, Ruifei & Deng, Yelin, 2023. "Optimization on charging of the direct hybrid lithium-ion battery and supercapacitor for high power application through resistance balancing," Energy, Elsevier, vol. 273(C).
    10. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    11. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    12. Yue, Tian & Shen, Boxiong & Gao, Pei, 2022. "Carbon material/MnO2 as conductive skeleton for supercapacitor electrode material: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).

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