The world record tandem efficiency has surpassed 34.6%, a significant milestone that exceeds the 29.1% efficiency limit for single-junction silicon solar cells. As photovoltaic (PV) technologies rapidly evolve, the modelling techniques used to predict their performance must also adapt. One widely used model, the 1-diode model, has long been favoured due to its simplicity and computational efficiency in simulating the behaviour of traditional silicon-based solar cells. However, as next-generation PV technologies—primarily perovskite solar cells and perovskite-silicon tandem cells—gain traction, the limitations of the 1-diode model are becoming more apparent. In this article, we explore the risks associated with relying on the 1-diode model for these emerging technologies.
- Overview of the 1-Diode Model
The 1-diode model is a popular mathematical representation used to describe the current-voltage (I-V) characteristics of a solar cell. This model assumes that the electrical behaviour of the cell can be represented by a single diode, along with a series of resistive components. While the 1-diode model is adequate for silicon-based solar cells, it oversimplifies many of the complex processes occurring in newer materials and advanced cell architectures.
The parameters used in the model include:
- Photocurrent (Iph): Current generated by light absorption.
- Saturation Current (I0): Leakage current under reverse bias.
- Diode Ideality Factor (n): Represents recombination processes within the cell.
- Series Resistance (Rs): Resistance to current flow through the cell.
- Shunt Resistance (Rsh): Resistance due to leakage currents.
While these parameters provide a good fit for traditional silicon-based solar cells, next-generation PV technologies exhibit unique physical phenomena that the 1-diode model struggles to capture.
- Risk 1: Inaccurate Performance Predictions for Perovskite Solar Cells
Perovskite solar cells are among the most promising next-gen PV technologies due to their high efficiency and low-cost manufacturing. However, perovskite materials exhibit behaviours such as:
- Ionic movement: The perovskite material experiences ionic migration, which affects its internal electric fields and introduces time-dependent behaviours that the 1-diode model does not account for.
- Hysteresis in I-V curves: Unlike silicon solar cells, perovskite solar cells often exhibit hysteresis in their I-V curves, meaning that their electrical characteristics change depending on the scan direction.
These factors can lead to significant discrepancies when predicting performance under real-world conditions, making the 1-diode model insufficient for reliable forecasting of perovskite cell efficiency or durability.
- Risk 2: Mismatch with Tandem Solar Cells
Tandem solar cells, which stack multiple materials with different bandgaps to capture a broader range of the solar spectrum, represent another significant advancement in PV technology. However, their complexity introduces several challenges for modelling:
- Multi-junction interaction: Tandem cells combine multiple sub-cells that interact with each other, creating complex current-matching issues. The 1-diode model, designed for single-junction cells, does not account for these interdependencies.
- Spectral mismatch: In tandem cells, each sub-cell responds to different portions of the solar spectrum. The 1-diode model lacks the ability to simulate these spectral interactions and energy loss mechanisms effectively.
As a result, using the 1-diode model to predict tandem cell performance can lead to inaccurate efficiency forecasts, particularly under non-standard illumination conditions.
- Risk 3: Temperature and Illumination Dependence in Tandem Solar Cells
In tandem solar cells, where two or more sub-cells are stacked to capture different portions of the solar spectrum, temperature and illumination variations can introduce significant challenges that the 1-diode model cannot effectively handle. Specifically, these variations lead to mismatch losses in 2-terminal (2T) tandem cells, where the current generated by each sub-cell must be perfectly matched for optimal performance.
- Temperature sensitivity: Each sub-cell in a tandem structure has a different temperature coefficient. For example, a silicon bottom cell and a perovskite top cell may respond differently to changes in temperature. As the temperature increases, the performance of the two cells may diverge, leading to mismatch losses that the 1-diode model cannot capture.
- Illumination variation: The sub-cells in a tandem structure also respond differently to changes in light intensity. In real-world conditions, illumination is not uniform across the entire solar spectrum, which can result in spectral mismatches between the sub-cells. The 1-diode model does not account for these spectral and illumination-dependent effects, leading to inaccurate performance predictions under varied environmental conditions.
These temperature and illumination dependencies in tandem cells are critical to understanding the mismatch losses that occur in 2T tandem cells, highlighting the need for more advanced modeling techniques to accurately simulate their behavior.
- Moving Toward More Accurate Models
As next-generation PV technologies continue to evolve, it is clear that more sophisticated modeling approaches are needed to accurately predict their performance. These models include:
- 2-diode models: These introduce a second diode to better account for recombination processes within the solar cell.
- Physics-based models: These models simulate complex interactions within the cell structure, such as the temperature dependence of series resistance and illumination variations.
While these advanced models provide a more accurate representation of next-gen PV technologies, they come with increased computational complexity. Therefore, a balance must be struck between model accuracy and practicality, depending on the application—whether it’s for lab-based research, field performance prediction, or commercial deployment.
- Conclusion
The 1-diode model has served the PV community well for traditional technologies such as silicon solar cells. However, its limitations become evident when applied to next-generation photovoltaic technologies. As the industry moves forward, it is crucial for researchers, manufacturers, and investors to adopt more sophisticated modelling techniques to avoid misjudging the potential and risks of emerging PV technologies. By doing so, we can fully realize the promise of these new innovations and optimize their deployment in real-world applications.