Correlating variability of modeling parameters with photovoltaic performance: Monte Carlo simulation of a meso-structured perovskite solar cell


The recent anti-carbon (fuel) campaigns being carried out globally have set the focus of most researchers on a similar trajectory aimed at developing renewable energy sources. The carbon footprint can be traced in almost every economic activity happening now, thereby leading to immense environmental degradation. Among various alternatives being sought, harvesting solar energy has been at the fore front. In fact, solar photovoltaics have been recently reported and have shown promising signs. Their technology involves the direct conversion of sunlight into electrical energy. As such, this perovskite-based type solar cell type has gained ever increasing prominence in the solar cell community.

To date, both meso-structured and planar-structured perovskite solar cells (PSCs) have been extensively investigated due to their potential for industrial applications. However, as of now, the optimal devise performance from investigated modeling parameters is not sufficient for the design of PSCs to have mass production. This has called for the application of stochastic approaches such as the Monte Carlo simulation (MCS). Unfortunately, a review of existing literature has shown that no Monte Carlo simulation has been formerly conducted for PSCs in correlating the variability of modeling parameters with cell efficiency.

In this view, researchers Dr. Hansong Xue, associate professor Erik Birgersson and Dr. Rolf Stangl at the National University of Singapore looked carefully on evaluating the photovoltaic performance of a perovskite solar cell as a function of its material properties, device geometry, and operating conditions. Their goal was to present a stochastic approach that could capture the probabilistic nature of uncertainties in the parameters and their relative influences on the cell efficiency for PSCs. Their work is currently published in the research journal, Applied Energy.

Briefly, the research team conducted a Monte Carlo simulation based on a mechanistic model for meso-structured perovskite solar cell to correlate the device performance with the variability of input modeling parameters. The presented sensitivity analysis was statistically performed in two different scenarios: first by varying the modeling parameters individually and second, by varying all of them simultaneously. The stochastic parameters were later ranked and quantified according to their contributions to the variation of the cell performance, thereby providing insights for optimum device performance.

The authors chiefly reported that the layer thickness of the hole and electron-transporting layers, and the hole mobility in the hole-transporting layer were the three most critical parameters influencing the cell performance. In fact, when this result was applied to a world record perovskite solar cell of 23.2% efficiency with parameter cross-validation, they were able to predict that the ‘world record’ efficiency could be further improved by 1.8% to achieve 25%.

In summary, National University of Singapore scientists presented a Monte Carlo simulation of a meso-structured perovskite solar cell model for the sensitivity analysis of the cell efficiency with the variability of material properties, device geometry, and operation condition. Overall, this work could provide insight on how to further optimize device efficiency for perovskite solar cells.

Correlating variability of modeling parameters with photovoltaic performance: Monte Carlo simulation of a meso-structured perovskite solar cell - Advances in Engineering

About the author

Hansong Xue is a research fellow and team leader for the thin-film device modeling team in the Solar Energy Research Institute of Singapore (SERIS). He received his PhD in Chemical & Biomolecular Engineering from National University of Singapore (NUS) in 2019. In his PhD thesis titled “Mathematical Modeling and Analysis of Perovskite Solar Cells” he established a multiscale modeling framework that quantifies morphology-performance relations, power-loss mechanisms and overall efficiency for perovskite solar cells.

He also holds a dual MS in Chemistry and Mathematics from Nanyang Technological University (2015) and NUS (2011), as well as a BS in Mathematics with first class honours from NUS (2009).

His research interests include solar photovoltaics, mainly perovskite and organic solar cells, perovskite on silicon or III-V tandem device, mathematical modelling of transport phenomena, optical modeling, computational chemistry, reaction kinetics for mechanistic study, photochemical flow reactions.

About the author

Erik Birgersson is currently an Associate Professor in the Department of Mechanical Engineering at the National University of Singapore. He graduated from the Royal Institute of Technology with a M.S. in Chemical Engineering in 1998 and a PhD in Fluid Mechanics in 2004. His research focuses on mathematical modelling and transport phenomena with applications in mainly electrochemical energy systems, solar cells, and biomedical devices.

As a consultant to ST Engineering, he helped design the Air+ smart mask, which has won awards such as the President’s Design Award 2015, the Reader’s and Editor’s Choice for the Most Innovative Product at the Straits Times Digital Awards 2016 and one of the Top 50 Engineering Feats Award in Singapore 2016. Besides research, he enjoys teaching and has been awarded 19 teaching awards in NUS (2006-2018).

About the author

Rolf Stangl is the Head of the Machine Learning & Novel Cell Concepts group at the Solar Energy Research Institute of Singapore (SERIS) at the National University of Singapore (NUS). He is also Principal Investigator of two public projects funded by the Energy Innovation Programme Office (EIPO) of Singapore, targeting passivated contact solar cells and perovskite on silicon tandem cells. Rolf Stangl holds a PhD degree in physics, for work on organic dye sensitized solar cells conducted at the Fraunhofer Institute for Solar Energy Systems (FhG ISE) in Freiburg, Germany. Prior to joining SERIS in 2011, he worked for several years as a Research Fellow on thin-film silicon and heterojunction solar cells at the Helmholtz Centre Berlin for Materials and Energy (HZB), Berlin, Germany.

He has (co)authored over 100 scientific papers (which have received more than 2300 citations), contributed to 5 books on solar cells, holds several patents in the area of silicon solar cells, and has established a widely used simulation programme (AFORS-HET) for heterojunction solar cells.


Hansong Xue, Erik Birgersson, Rolf Stangl. Correlating variability of modeling parameters with photovoltaic performance: Monte Carlo simulation of a meso-structured perovskite solar cell. Applied Energy, volume 237 (2019) page 131–144.

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