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Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology

Author

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  • Qadeer Akbar Sial

    (Department of Advanced Materials Chemistry, Korea University, Sejong 339-700, Republic of Korea
    These authors contributed equally to this work.)

  • Usman Safder

    (School of Chemical and Bioprocess Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
    These authors contributed equally to this work.)

  • Shahid Iqbal

    (Engineering Research Institute, Ajou University, Suwon 16499, Republic of Korea
    Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea)

  • Rana Basit Ali

    (Department of Energy Systems Research, Ajou University, Suwon 16499, Republic of Korea)

Abstract

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development.

Suggested Citation

  • Qadeer Akbar Sial & Usman Safder & Shahid Iqbal & Rana Basit Ali, 2024. "Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology," Sustainability, MDPI, vol. 16(4), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1516-:d:1337193
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    References listed on IDEAS

    as
    1. 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).
    2. Zhou, Yanting & Wang, Yanan & Wang, Kai & Kang, Le & Peng, Fei & Wang, Licheng & Pang, Jinbo, 2020. "Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors," Applied Energy, Elsevier, vol. 260(C).
    3. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
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