Kouroudis Ioannis, Tanko Kenedy Tabah, Karimipour Masoud, Ali Aziz Ben, Kumar D Kishore, Sudhakar Vediappan, Gupta Ritesh Kant, Visoly-Fisher Iris, Lira-Cantu Monica, Gagliardi Alessio
Department of Electrical Engineering, School of Computation, Information and Technology, Technical University of Munich, Hans-Piloty Strasse 1, 85748 Garching bei Munich,Germany.
Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and The Barcelona Institute of Science and Technology, 08193 Bellaterra, Barcelona, Spain.
ACS Energy Lett. 2024 Mar 19;9(4):1581-1586. doi: 10.1021/acsenergylett.4c00328. eCollection 2024 Apr 12.
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.
在实际户外条件下进行耗时的降解研究这一限制因素，严重阻碍了钙钛矿太阳能电池（PSC）的商业发展。这些研究是确定器件寿命的必要步骤，而这正是PSC传统上存在问题的领域。在这项工作中，我们证明了通过采用加速室内稳定性分析，可以预测PSC的户外降解行为。利用快速且准确的机器学习算法和数学分解流程，这种预测成为可能。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，从而揭示户外降解途径。我们的方法并非特定于PSC，可扩展到其他光伏技术，在这些技术中，降解及其机制是其广泛应用的关键因素。
