Microsystem Technologies

, Volume 22, Issue 7, pp 1811–1820 | Cite as

Optimization method for designing multimodal piezoelectric MEMS energy harvesters

Technical Paper


Energy harvesters (EH) are devices that convert environmental energy (i.e. thermal, vibrational or electromagnetic) into electrical energy. One of the most promising solutions consists in transforming energy from vibrations using a piezoelectric material placed onto a mechanical resonator. The intrinsic drawback of this solution is the typically high quality factor of the device which works effectively only within a narrow bandwidth. To overcome this limitation it is possible to tune the mechanical resonance of the device, to introduce non-linear elements (e.g. magnets) or to design the mechanical resonator with a multimodal behaviour. In ultra low power applications the aspect of integration is of utmost importance and so micro electro-mechanical systems (MEMS)-based EHs are preferable. Within this scenario the multimodal solution is the more suitable considering the technological constraints imposed by the micromachining manufacturing process. In this paper, we describe the optimization of a given multimodal mechanical geometry in order to maximize the number of resonances within a certain frequency band. The proposed optimization is finite element method (FEM)-based and it uses modal and harmonic simulations for both selecting the useful modes and then designing the device in a way that presents those modes within a predefined frequency range. This mechanical optimization is the first step for maximizing the output power of a multimodal piezoelectric energy harvester. The second step focuses on the optimization of the piezoelectric transducer geometry targeting the resonant modes defined in the first step. The optimization procedure is applied to an array of cantilever used as a case study.


Resonant Frequency Energy Harvester Smart City Micro Electro Mechanical System Mechanical Resonator 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Center for Materials and Microsystems (CMM)Fondazione Bruno Kessler (FBK)TrentoItaly
  2. 2.Institute of Sensor and Actuator SystemsTU WienViennaAustria

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