Piezoelectric Energy Harvester Microsystems

The increasing occurrence of unprecedented natural disasters attributed to climate change serves as a constant reminder of the necessity to decrease reliance on fossil fuels. In order to harvest renewable energy sources as a
solution, our research proposes an innovative method of extracting maximum energy from ocean waves via piezoceramic material. In this work, the dynamics of the waves are modelled by a comprehensive data-driven
model, which characterizes small-scale mechanical motions. The approach involves using machine learning to model ocean wave characteristics such as wave amplitude, wavelength and wave period. This model can also
handle multi-dimensional and multi-variety data obtained by uncertain ocean environments. Our proposed microdevice is validated using advanced techniques, including artificial neural networks and deep learning, to
optimize its design in terms of structure and geometry sizes for efficient energy harvesting. The fabricated MEMSscale chip from the services of CMC (Canadian Microelectronics Corporation) is being measured and
characterized in our laboratory equipped with state-of-the-art analyzing instruments to confirm the supervior performance. In addition, a hybrid conditioning circuit, which can convert the generated AC voltage to a desirable
DC constant voltage output supporting the load of different environmental sensors, is actively being developed in the simulation and testing stage.

Faculty Supervisor:

Lihong Zhang;Mohammad Al Janaideh

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Green/Alternative Energy; Energy and Utilities; Oil and Gas

University:

Memorial University of Newfoundland

Program: