Multistress testing of OPV modules for accurate predictive aging and reliability predictions

Stoichkov, V., Kumar, D., Tyagi, P. and Kettle, J. (2018) Multistress testing of OPV modules for accurate predictive aging and reliability predictions. IEEE Journal of Photovoltaics, 8(4), pp. 1058-1065. (doi: 10.1109/JPHOTOV.2018.2838438)

Abstract

Organic photovoltaic (OPV) degradation remains a complex challenge and previous studies have shown the degradation to be a function of multiple stresses, so it can be inaccurate to predict failure rates using single stress tests. In this paper, a new testing methodology whereby multiple stresses are applied simultaneously using a “design of experiment (DOE) approach” is reported and used for predictive aging of modules. Multistress data are used for predictive aging of OPV modules under different stress levels; a general log-linear life model has been adapted and applied in order to predict the life of OPV modules and this is compared to experimental data, which show that a close estimation of simulated lifetime is obtained (within 18% accuracy). The life test models can be used for predicting aging of OPV modules in different geographic locations and could be used to account for different degradation rates due to seasonal climatic variations. Furthermore, by using the DOE data, we show how the major stress factors can be screened and their statistical significance upon degradation quantified using analysis of variance. One of the potential benefits of using this approach for OPV degradation studies is that additional factors could be added to study the impact on degradation to provide a more comprehensive study.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kettle, Professor Jeff
• Authors: Stoichkov, V., Kumar, D., Tyagi, P., and Kettle, J.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Journal of Photovoltaics
• Publisher: IEEE
• ISSN: 2156-3381
• ISSN (Online): 2156-3403
• Published Online: 11 June 2018

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