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A Multi Harvester with Hydrogen Fuel Cell for Outdoor Applications

  • Davide Brunelli
  • Michele Magno
  • Danilo Porcarelli
  • Luca Benini
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 289)

Abstract

Energy availability and long term operation are key challenges for wireless sensor networks and for all the applications where the devices are battery operated. For this reason energy harvesting is becoming very important for powering ubiquitously deployed sensor networks and mobile electronics. One of most important goal for the next generation of power supply units for standalone embedded systems is to power nearly perpetually the devices when the scavenger is exposed to reasonable environmental energy conditions. However, due to the unpredictable nature of the environmental sources, prolonged lacks of energy intake usually happen. The last frontiers of perpetual operating systems is combining different energy harvesters in a single unit and using green energy supply with high energy density as micro hydrogen fuel cells. In this paper we introduce a Smart Power Unit (SPU) for embedded system which incorporates energy harvesters from sun and wind and uses hydrogen fuel cell as alternative energy storage. The power unit can work as a long-term battery or providing serial communication to exchange power information and to perform power management. In fact the core of the SPU is an ultra low power micro controller which is in charge to do the power activities such as Maximum Power Point Tracking for the harvesters, fuel cell activation, energy prediction, adaptive power management on board, battery monitoring and communications with powered systems. Experimental results and simulations shows the high efficiency (up to 90 %) of the power conversion subsystem. Finally a real deployment in a structural health monitoring site in Switzerland shows as the energy neutral condition is achieved on field.

Keywords

Fuel Cell Wireless Sensor Network Power Unit Energy Harvester Structural Health Monitoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the FP7 GENESI project (Green sEnsor NEtworks for Structural monItoring) funded grant number 257916.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Davide Brunelli
    • 1
  • Michele Magno
    • 2
  • Danilo Porcarelli
    • 2
  • Luca Benini
    • 2
  1. 1.University of TrentoTrentoItaly
  2. 2.University of BolognaBolognaItaly

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