A multimodal hybrid energy harvester based on piezoelectric-electromagnetic mechanisms for low-frequency ambient vibrations

By R. M. Toyabur, M. Salauddin, Hyunok Cho, J. Park. Energy Conversion and Management, 2018

222 citations recorded by the source corpus.

Abstract In this paper, we proposed and experimentally validated a vibration-based multimodal hybrid piezoelectric–electromagnetic energy harvester having multiple mechanical degrees-of-freedom. This multimodal hybrid energy harvester (MHEH) has a unique design, which helps it to achieve multiple close resonant modes of vibration in a certain frequency range. Using a low-spring-stiffness material (polyacrylate) as a substrate assists MHEH in reducing the higher resonant frequencies into a low frequency range. The two combined conversion mechanisms (piezoelectric–electromagnetic) are exploited to obtain higher output power from low input accelerations at ambient vibrations. The finite element method simulation model is employed to predict and optimize the mode shapes of the proposed MHEH for different vibration modes. The simulation and experimental result imply that the proposed MHEH can operate at four resonant modes of vibration in the range of 12–22 Hz, which are concentrated around 12, 15, 17, and 22 Hz. An MHEH prototype is fabricated, where four lead-zirconate-titanate elements are used as piezoelectric materials, and NdFeB magnets with conductive coils are used as electromagnetic parts in the same system. Here, a single piezoelectric generator can produce a maximum of 250.23 µW of power across an optimum load of 90 KΩ at the 3rd resonant mode (17 Hz) under 0.4 g (3.92 ms −2 ) acceleration. On the other hand, a single electromagnetic generator can deliver a maximum power of 244.17 µW to a 10 Ω optimum load under the same conditions. Meanwhile, all eight generators of the MHEH operate simultaneously at their respective resonant frequencies.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/7d850e997aa90c223fdfdd862f239247750d3409

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

  1. Chengkuo Lee
  2. Huicong Liu
  3. Eric M. Yeatman
  4. Hailing Fu
  5. Lining Sun
  6. Xinge Guo
  7. Long Liu
  8. Tianyiyi He

In Electrical and Electronic Engineering

  1. Chengkuo Lee
  2. Huicong Liu
  3. Hailing Fu
  4. Eric M. Yeatman
  5. Shahrzad Towfighian
  6. Wei Yang

In Mechanical Engineering

  1. Chengkuo Lee
  2. Huicong Liu
  3. Hailing Fu
  4. Eric M. Yeatman
  5. Lining Sun
  6. Shahrzad Towfighian
  7. Wei Yang

Citations in the corpus