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Self-powered wireless sensing system driven by daily ambient temperature energy harvesting

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  • Thi Kim Tuoi, Truong
  • Van Toan, Nguyen
  • Ono, Takahito

Abstract

This work demonstrates a self-powered wireless IoT sensing system driven by daily ambient temperature energy harvesting. A novel approach using a thermoelectric generator (TEG) which harvests thermal energy from daily ambient temperature fluctuations into electricity as a power source for wireless IoT devices is proposed and investigated. A harvested temperature difference across the TEG is created by employing a phase change material (PCM) which allows the temperature value to be maintained near the melting point at the phase transition during the ambient temperature fluctuations. Experimental results indicated that the harvested temperature differences are 10 °C and 5 °C for 300 ml and 30 ml of PCM, respectively, during the ambient temperature variations from 5 °C to 25 °C. By utilizing the ambient temperature fluctuations between day and night times, an average maximum output power of TEG measured for 3 days is 340 µW and DC-DC electronic conversion efficiency is 28.3%. The total storable energy of approximately 1.46 J for a day is estimated. A capacitor as an energy storage unit could be fully charged and its usable energy is calculated at 0.686 J. The current consumption of 2.1 µA for every transmit cycle is estimated. A self-powered wireless IoT sensing system driven by realistic ambient temperature variations able to sense the ambient temperature as well as the ambient humidity and transmit to a computer via RF communication is demonstrated successfully.

Suggested Citation

  • Thi Kim Tuoi, Truong & Van Toan, Nguyen & Ono, Takahito, 2022. "Self-powered wireless sensing system driven by daily ambient temperature energy harvesting," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922001441
    DOI: 10.1016/j.apenergy.2022.118679
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    1. Ze-Qi Lu & Long Zhao & Hai-Ling Fu & Eric Yeatman & Hu Ding & Li-Qun Chen, 2024. "Ocean wave energy harvesting with high energy density and self-powered monitoring system," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Wei, Zhichen & Calautit, John Kaiser, 2024. "Field experiment testing of a low-cost model predictive controller (MPC) for building heating systems and analysis of phase change material (PCM) integration," Applied Energy, Elsevier, vol. 360(C).

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