Practical PV energy harvesting under real indoor lighting conditions. (August 2021)

• Record Type: Journal Article
• Authors: Politi, Bastien; Parola, Stéphanie; Gademer, Antoine; Pegart, Diane; Piquemil, Marie; Foucaran, Alain; Camara, Nicolas
• Is Part Of: Solar energy. Volume 224(2021)
• Page Start: 3
• Page End: 9
• Publication Date: 2021-08
• Subjects: Energy harvesting -- Calculation model -- Indoor light -- Indoor PV -- Mixed light -- Real life conditions; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.05.084
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Energy harvesting calculation model aiming towards autonomous consumer devices. Model using two intrinsic solar cell characteristics and light environment spectra. Method applied to standard artificial lights and natural indoor light environments. Results with relatively low errors in real indoor environments. Calculations validated using an instrumented energy harvesting prototype. Abstract: Indoor light can be used as a new energy source to power µW low consumption wireless sensor networks (WSNs), but for wireless electronic devices consuming tens of mW, it is still challenging. The challenge comes from the low level of irradiance and from the several kinds of source combinations varying in time (multi-spectral direct, reflective, and scattered mix of artificial and natural light). This article describes a simple and reliable method that provides a model-based evaluation of the harvestable energy from any real indoor light environment. This method uses 'real condition' indoor light spectral measurements with a spectrometer as well as 'controlled condition' optoelectrical characteristics of the photovoltaic solar cells. The model-based evaluation of the harvestable energy has been compared with real microsource prototypes based on commercial photovoltaic cells powering commercial wireless e-ink display (more than 10 mW consumption averaged on a day). In this article, we show that it is possible to evaluate the harvestable energy, for several days of indoor light exposure, with an error lower than 6%. Our method, with such an accuracy range, will be a helpful tool to assist engineers and researchers in designing light energy harvesting systems and more generally could find a wide application in the growing IoT ecosystem.