Solar incidence determination using ambient light sensors and a machine learning approach for space solar cell characterization
Lennart Reb a
a Lehrstuhl für Funktionelle Materialien, Physik-Department, Technische Universität München, James-Franck-Str. 1, 85748 Garching, Germany.
Proceedings of New Generation Photovoltaics for Space (PVSPACE)
Online, Spain, 2022 June 21st - 22nd
Organizers: Narges Yaghoobi Nia, Aldo Di Carlo, Luigi Schirone and Mahmoud Zendehdel
Poster, Lennart Reb, 013
Publication date: 8th June 2022

The advent of novel material thin-film solar cells with their intrinsically low thickness and low-temperature processability opens up the avenue of lightweight and flexible solar cells for various new application fields. The combination of their today’s high efficiency and their low weight results in magnificent power-per-mass values, compared to classical space solar cells, that attest to them a high intrinsic fitness for becoming the next generation of space solar cells.

Previous near-space experiments in the upper atmosphere have shown the suitability of organic and perovskite solar cells with promising results in terms of their functionality and ability to generate electric power in harsh environmental conditions. We presented the first electrical characterization of these solar cell types in space at orbital altitudes [1]. However, the general interpretability of solar cell measurements is limited if the incident solar power is unknown, which is the usual case in real-world applications, apart from controlled laboratory conditions. In order to convert the measurements into quantitative performance data, the at the time present solar incident power needs to be well known and used as a solid ground for interpretation of the solar cell measurements.

Here, we present a new method of attitude determination with respect to the solar position based on parallelized ambient light sensor measurements. The measurements are obtained from the sounding rocket experiment Organic and Hybrid Solar Cells in Space during the MAPHEUS-8 mission [2]. In detail, we optimize the solar position evolution model during the flight using machine learning methods on the synchronized and parallelized ambient light sensor measurements and estimate the uncertainties of our solar position evolution reconstruction. The comparison with independent attitude estimates based on camera and inertia measurements shows promising agreement, mostly within 5°. Our simple sensor-array-based solar tracking method allows reconstruction of the specific solar irradiance conditions for our space solar cells with high precision. The basic principle of this method can be applied to various light sensor configurations and illumination conditions.

We use our own and third party cookies for analysing and measuring usage of our website to improve our services. If you continue browsing, we consider accepting its use. You can check our Cookies Policy in which you will also find how to configure your web browser for the use of cookies. More info