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

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Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells
ACS Energy Letters ( IF 19.3 ) Pub Date : 2024-03-19 , DOI: 10.1021/acsenergylett.4c00328
Ioannis Kouroudis ₁ , Kenedy Tabah Tanko ₂ , Masoud Karimipour ₂ , Aziz Ben Ali ₁ , D Kishore Kumar ₃ , Vediappan Sudhakar ₃ , Ritesh Kant Gupta ₃ , Iris Visoly-Fisher ₃ , Monica Lira-Cantu ₂ , Alessio Gagliardi _{1,

4}

Affiliation

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.

中文翻译：

基于人工智能的小波辅助预测钙钛矿太阳能电池的长期户外性能

由于在真实户外条件下进行耗时的降解研究的限制，钙钛矿太阳能电池（PSC）的商业开发已被大大推迟。这些是确定器件寿命的必要步骤，而器件寿命是 PSC 传统上受到影响的领域。在这项工作中，我们证明可以通过采用加速室内稳定性分析来预测 PSC 的室外降解行为。使用快速而准确的机器学习算法和数学分解流程可以实现预测。通过使用不同的室内稳定性数据集训练算法，我们可以确定最相关的应力因素，从而揭示室外退化路径。我们的方法并不特定于 PSC，并且可以扩展到其他光伏技术，其中退化及其机制是其广泛采用的关键要素。

更新日期：2024-03-19

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