Combined power extraction with adaptive power management module for increased piezoelectric energy harvesting to power wireless sensor nodes

Author(s):  
Zheng Jun Chew ◽  
Meiling Zhu
2011 ◽  
Vol 22 (18) ◽  
pp. 2215-2228 ◽  
Author(s):  
Jayant Sirohi ◽  
Rohan Mahadik

There has been increasing interest in wireless sensor networks for a variety of outdoor applications including structural health monitoring and environmental monitoring. Replacement of batteries that power the nodes in these networks is maintenance intensive. A wind energy–harvesting device is proposed as an alternate power source for these wireless sensor nodes. The device is based on the galloping of a bar with triangular cross section attached to a cantilever beam. Piezoelectric sheets bonded to the beam convert the mechanical energy into electrical energy. A prototype device of size approximately 160 × 250 mm was fabricated and tested over a range of operating conditions in a wind tunnel, and the power dissipated across a load resistance was measured. A maximum power output of 53 mW was measured at a wind velocity of 11.6 mph. An analytical model incorporating the coupled electromechanical behavior of the piezoelectric sheets and quasi-steady aerodynamics was developed. The model showed good correlation with measurements, and it was concluded that a refined aerodynamic model may need to include apparent mass effects for more accurate predictions. The galloping piezoelectric energy-harvesting device has been shown to be a viable option for powering wireless sensor nodes in outdoor applications.


Author(s):  
Alexander Schlichting ◽  
Rashi Tiwari ◽  
Ephrahim Garcia

Much of the work on ambient energy harvesting currently focuses on maximizing the efficiency of the power extraction method or increasing the power output. However, an important goal is still out of reach: to produce sufficient and sustained power for wireless sensor nodes and their associated components. Achieving this goal will significantly impact the fields of structural health monitoring, secure location surveillance and a multitude of other pertinent applications. While many energy harvesters can power these electronics with a constant energy source, most ambient sources for desired wireless sensor networks only provide intermittent energy. One method for increasing the robustness and versatility of energy harvesting systems would utilize multiple energy sources simultaneously. In order to achieve this, the problem of effectively combining multiple energy harvesting sources, specifically an AC and a DC source, is thoroughly explored with the experimental analysis of proposed circuit configurations. Also, a multi-source energy harvesting circuit configuration is proposed.


Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3776 ◽  
Author(s):  
Juan Carlos Rodriguez ◽  
Valeria Nico ◽  
Jeff Punch

Electromagnetic Vibration Energy Harvesting (EM-VEH) is an attractive alternative to batteries as a power source for wireless sensor nodes that enable intelligence at the edge of the Internet of Things (IoT). Industrial environments in particular offer an abundance of available kinetic energy, in the form of machinery vibrations that can be converted into electrical power through energy harvesting techniques. These ambient vibrations are generally broadband, and multi-modal harvesting configurations can be exploited to improve the mechanical-to-electrical energy conversion. However, the additional challenge of energy conditioning (AC-to-DC conversion) to make the harvested energy useful brings into question what specific type of performance is to be expected in a real industrial application. This paper reports the operation of two practical IoT sensor nodes, continuously powered by the vibrations of a standard industrial compressor, using a multi-modal EM-VEH device, integrated with customised power management. The results show that the device and the power management circuit provide sufficient energy to receive and transmit data at intervals of less than one minute with an overall efficiency of about 30%. Descriptions of the system, test-bench, and the measured outcomes are presented.


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