Researchers at Newcastle University in the UK have developed a low cost solar cell for ambient light with AI control to power Internet of Things (IoT) devices.
The high-efficiency (38%) ambient photovoltaic based on sustainable non-toxic materials and present a full implementation of a long short-term memory (LSTM) based energy management. This uses on-device prediction on IoT sensors solely powered by ambient light harvesters.
The power is supplied by dye-sensitised photovoltaic cells based on a copper(II/I) electrolyte with an unprecedented power conversion efficiency at 38% and 1.0 V open-circuit voltage at 1000 lux from a fluorescent lamp.
The on-device LSTM predicts changing deployment environments and adapts the devices’ computational load accordingly to perpetually operate the energy-harvesting circuit and avoid power losses or brownouts. Merging ambient light harvesting with artificial intelligence presents the possibility of developing fully autonomous, self-powered sensor devices that can be utilized across industries, health care, home environments, and smart cities.
“Our research marks an important step towards making IoT devices more sustainable and energy-efficient. By combining innovative photovoltaic cells with intelligent energy management techniques, we are paving the way for a multitude of new device implementations that will have far-reaching applications in various industries,” said Dr Marina Freitag, Principal Investigator at the School of Natural and Environmental Sciences at Newcastle University.
The long short-term memory (LSTM) artificial neural network predicts changing deployment environments and adapt the computational load of IoT sensors accordingly. This dynamic energy management system enables the energy-harvesting circuit to operate at optimal efficiency, minimizing power losses or brownouts.
An array of seven 3.2 cm2 with a total area of 22.4 cm2 of the dye-sensitised solar cells is used to power IoT sensors based on the FireBeetle ESP32 microcontroller. The devices dynamically recognise recurring patterns in illuminance by comparing real-time data to pre-trained artificial neural networks.
The sensors dynamically decide on the execution of computational tasks to operate the energy harvesting circuit at high efficiency. The dye-sensitised solar cells convert ambient light at 38% power conversion efficiency and 1.0 V open-circuit voltage. Transient photovoltage/-current and electrochemical impedance tests highlight the importance of the copper electrolyte to suppress recombination across the TiO2|dye|electrolyte interface.