Objective
Currently, most Internet of Things (IoT) devices and low power consumer electronics devices are powered by primary non-rechargeable batteries, which require periodic replacement and maintenance owing to their relatively short lifespans as power sources. Considering the advancement of IoT ecosystems for smart homes, offices, factories and retail (by 2027, an estimated 30 billion IoT devices will be in use), powering a huge number of IoT devices solely from primary batteries would not be practically sustainable from an environmental, resource, safety and cost perspective.
Energy harvesting technology has the potential to overcome these issues through providing self-sufficient, autonomous, low-power for IoT electronics by harvesting available unused energy. A promising energy harvesting technology is through light energy harvesting (LEH) of ambient indoor light using photovoltaic technology which is capable of generating power even under indoor low-light conditions.
Within this class of photovoltaic devices are organic photovoltaics (OPV), which, unlike inorganic silicon, have various inherent advantages such as lightweight, flexibility, solution processability and cost-effective large area manufacturing capabilities. Moreover, OPVs can convert weak indoor light into electricity more efficiently than other PV technologies due to their spectral tunability and higher optical absorptivity as well as low leakage currents which are desirable for efficient operation of PV cells as they minimise power losses and improve the fill factor, especially at low-light intensities.
The main focus of the ENLIGHTENED project is to increase the potential of PV technology for low-power, low-light applications by demonstrating the viability and potential of OPV-based LEH technology, to meet the power and energy requirements of a diverse range of customers representing Retail, Property Tech and Consumer Electronics.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencesbiological sciencesecologyecosystems
- natural scienceschemical sciencesinorganic chemistrymetalloids
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energysolar energyphotovoltaic
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Programme(s)
Funding Scheme
HORIZON-IA - HORIZON Innovation ActionsCoordinator
582 13 Linkoping
Sweden
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.