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Micromachines 2018, 9(4), 161; https://doi.org/10.3390/mi9040161

Electric Power Self-Supply Module for WSN Sensor Node Based on MEMS Vibration Energy Harvester

1
Graduate School at Shenzhen, Tsinghua University, University Town of Shenzhen, Shenzhen 518055, China
2
Tsinghua-Berkeley Shenzhen Institute, University Town of Shenzhen, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Received: 15 January 2018 / Revised: 21 March 2018 / Accepted: 28 March 2018 / Published: 1 April 2018
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Abstract

This paper proposes an electric power self-supply module for the wireless sensor network (WSN) sensor node. The module includes an electromagnetic vibration energy harvester based on micro-electro-mechanical system (MEMS) technology and a processing circuit. The vibration energy harvester presented in this paper is fabricated by an integrated microfabrication process and consists of four similar and relatively independent beam vibration elements. The main functions of the processing circuit are to convert the output of the harvester from unstable alternating current (AC) to stable direct current (DC), charge the super capacitor, and ensure the stable output of the super capacitor. The preliminary test results of the harvester chip show that the chip can output discontinuous pulse voltage, and the range of the voltage value is from tens to hundreds of millivolts in the vibration frequency range of 10–90 Hz. The maximum value that can be reached is 563 mV (at the vibration frequency of 18 Hz). The results of the test show that the harvester can output a relatively high voltage, which can meet the general electric power demand of a WSN sensor node. View Full-Text
Keywords: wireless sensor network; vibration energy harvesting; MEMS; electromagnetic conversion wireless sensor network; vibration energy harvesting; MEMS; electromagnetic conversion
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Zhang, W.; Dong, Y.; Tan, Y.; Zhang, M.; Qian, X.; Wang, X. Electric Power Self-Supply Module for WSN Sensor Node Based on MEMS Vibration Energy Harvester. Micromachines 2018, 9, 161.

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