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Details of Grant 

EPSRC Reference: EP/S02106X/1
Title: Energy Harvesting Triboelectric Nano-Generators for the Internet-of-Things
Principal Investigator: Silva, Professor SRP
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Georgia Institute of Technology Jaguar Land Rover MAS Innovations Ltd
National Physical Laboratory QinetiQ Soochow University China
Department: ATI Electronics
Organisation: University of Surrey
Scheme: Standard Research
Starts: 01 June 2019 Ends: 31 May 2023 Value (£): 632,627
EPSRC Research Topic Classifications:
Energy Efficiency
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Apr 2019 Engineering Prioritisation Panel Meeting 9 and 10 April 2019 Announced
Summary on Grant Application Form
Next generation technologies, such as the IoT and 5G technology, are shaping to enhance the standard of life of people by creating a digitally connected world, in which the productivity, health, and communication will be vastly improved. This involves integrating sensors, intelligent circuits and miniature electronic devices into day to day objects around us, including the human body, clothing, buildings, vehicles and streets etc. Such systems become increasingly feasible due to the advancements in low-power electronics and IoT technologies, however, powering these electronics with the required complexity, flexibility, mobility and self-powered capabilities remains one of the key challenges in the modern era. Scavenging power from freely available ambient mechanical energy sources, such as human motion, wind, wave energy and machine vibrations, has been proven to be a viable approach to fulfil such energy and performance requirements.

The triboelectric Nanogenerator (TENG) is one of the leading candidates to emerge as a potential energy source for powering autonomous IoT applications. These devices have shown the capability of capturing waste mechanical energy from ambient sources and easily producing a few Watts of output power, with high conversion efficiencies reported. However, knowledge of the electromagnetic behaviour of TENGs and the exact way they operate has been lacking in the past. Consequently, the relationship between the structural, material and motion parameters with the output power has not been adequately studied. This has resulted in non-optimised TENG architectures which suffer from relatively low, instantaneous and irregular output power, along with an impedance mismatch between the TENG and the output applications. Such issues decrease the output power of the TENG and significantly reduce its efficiency. This in turn associates with numerous other issues such as elevated cost, higher carbon footprint, larger device size and unreliable power supply.

Recently, we introduced the distance-dependent electric field (DDEF) model, the first analytical theoretical model to fully describe the working principles of TENGs, using Maxwell's equations. This model has been proven to accurately predict the output behaviour of different TENG working modes and has been successfully applied to develop optimisation strategies for simple planar TENGs, significantly reducing most issues described above.

In the proposed project, we will use the DDEF model to optimise material, device and motion parameters of TENGs to develop autonomous energy harvesters for IoT applications such as health sensors, wireless communication networks, portable and wearable electronics. We will first assess the energy requirements of IoT devices and design TENGs with suitable efficiencies to capture that energy from ambient sources. These devices are then finetuned to obtain the ideal size, shape, and material type, which will fit the applications while providing optimum electric field distribution, resulting in increased power outputs. We will use commonly available, low cost and flexible triboelectric polymers (eg: nylon, PET) as TENG layers, and further use scalable low-cost manufacturing techniques. Nanotechnology based surface improvements will be conducted to further improve the efficiency of these devices. The suggested improvements will increase the output power by about 100% compared to a non-optimised device, as evident from our simulation and calculation results. To ensure a non-interrupted regular power supply, we will integrate many TENG units with calculated phase differences, which would result in a near DC output current. Finally, we will combine the power management circuits and energy storage units (eg: supercapacitors and flexible batteries) along with the TENG to the IoT module, to assemble the fully integrated self-powered IoT devices.

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