Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells

Sep 21, 2026

Commercial perovskite solar cells face delayed deployment because outdoor lifetime assessment requires time-consuming degradation studies under realistic conditions. This work shows outdoor degradation behavior can be predicted from accelerated indoor stability analyses using a fast and accurate machine-learning pipeline combined with mathematical decompositions. Training across multiple indoor stability datasets identifies the most relevant stress factors and clarifies outdoor degradation pathways. The approach generalizes beyond PSCs to other photovoltaic technologies where degradation mechanisms govern adoption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

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Abstract

Background and motivation

Prior approaches and gaps

Methodology and machine learning pipeline

Experimental data and device testing

This article is licensed under CC-BY 4.0 [http://pubs.acs.org/journal/aelccp](http://pubs.acs.org/journal/aelccp) Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells Ioannis Kouroudis,∥ Kenedy Tabah Tanko,∥ Masoud Karimipour, Aziz Ben Ali, D. Kishore Kumar, Vediappan Sudhakar, Ritesh Kant Gupta, Iris Visoly-Fisher,* Monica Lira-Cantu, * and Alessio Gagliardi * Cite This: ACS Energy Lett. 2024, 9, 1581−1586 Read Online | | | | | | | | --- | --- | --- | --- | --- | --- | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | ABSTRACT: The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption. S ingle-junction perovskite-based solar cells (PSCs) have demonstrated certified power conversion efficiencies (PCEs) above 26%.1 With PCEs on par with those of well-established commercial PV technologies, the research focus is now aimed at improving PSCs’ operational stability, which is approximately 20 years for commercial silicon PV. Although a consensus on accelerated stability tests including various stress factors has been published,2 a strategy that permits the prediction of the PSCs’ outdoor performance and lifetime from accelerated indoor aging tests is currently missing. Operational conditions outdoors include diurnal light/dark cycling and varying temperatures, illumination intensity, and spectrum during sunlight hours. While the PSC performance dependence on each of these factors was determined, it is highly sensitive to combinations of them,3 making predictions of outdoor daily energy yield rather complex.4 Further complication arises from well-known PSC transient performance variations on time scales from seconds to days, superimposed on nonreversible, long-term degradation.5−7 Several attempts to predict PSC lifetimes based on indoor tests have been published, such as testing under constant illumination,8 a combination of continued 1 sun illumination © 2024 The Authors. Published by American Chemical Society (ISOS-L1 protocol) tests and dark storage (ISOS-D protocol) tests,9 or damp heat testing at 85 °C and 85% relative humidity.10 These works predicted lifetimes of 5−20 years for PSCs of various architectures, demonstrating the need for accelerated testing. A recent publication described good correlation between modeling based on PSC indoor tests of light intensity and temperature-dependent performance dynamics and its long-term, nonreversible outdoor degradation.11 Several studies simulated outdoor conditions in lab tests.12, 13 However, direct predictions of the detailed time-and climate conditions- dependent outdoor PSC performance including both daily power output and nonreversible degradation, based on accelerated indoor tests, are currently lacking. Received: January 31, 2024 Revised: March 9, 2024 Accepted: March 12, 2024 Published: March 19, 2024 [https://doi.org/10.1021/acsenergylett.4c00328](https://doi.org/10.1021/acsenergylett.4c00328) 1581 ACS Energy Lett. 2024, 9, 1581−1586 Letter Figure 1. Methodology (a) and typical in

FAQs

Why is outdoor degradation/lifetime study difficult for perovskite solar cells?

Outdoor lifetime determination requires time-consuming degradation studies under realistic conditions, which slows commercial progress.

How does the paper predict outdoor performance without direct long outdoor tests?

It uses accelerated indoor stability analyses, training machine-learning models to map indoor stress data to outdoor degradation behavior and time-dependent efficiency patterns.

What key idea makes the prediction pipeline effective?

The models are trained using different combinations of indoor accelerated tests so the algorithm can infer which stress factors most strongly drive outdoor degradation pathways.

Sep 21, 2026

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