• Energy Research

AbstractAutomated inspection plays an important role in monitoring large‐scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules, but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurements of modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here, we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error (MAE) of 9.0 ± 8.4WP (4.0 ± 3.7%). Then, we show that deep learning can be used to train a model that performs significantly better (7.3 ± 6.5WP or 3.2 ± 2.7%) and propose a variant of class activation maps to obtain the per cell power loss, as predicted by the model. With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code, and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on‐site measurements showing multiple modules at once.

Solution-processed organic and inorganic semiconductors offer a promising path towards low-cost mass production of solar cells. Among the various material systems, solution processing of multicomponent inorganic semiconductors offers considerable promise due to their excellent electronic properties and superior photo- and thermal stability. This review surveys the recent developments of “all solution-processed” copper–indium (–gallium)-chalcogenide (CuInS2, CuInSe2 and Cu(In, Ga)(Se, S)2) chalcopyrites and copper–zinc–tin-chalcogenide (Cu2ZnSnS4 and Cu2ZnSnSe4 (CZTS(e))) kesterite solar cells. A brief overview further addresses some of the most critical material aspects and associated loss mechanisms in chalcopyrite and kesterite devices. Today's state-of-the-art performance as well as future challenges to achieve low-cost and environmentally friendly production is discussed.

AbstractDuring the last years, the development of new active materials has led to constant improvement in the power conversion efficiency (PCE) of solution‐processed organic photovoltaics (OPV) to nowadays record values above 17% on small lab cells. In this work, we show the developments and results of a successful upscaling of such highly efficient OPV systems to the module level on large areas, which yielded two new certified world record efficiencies, namely, 12.6% on a module area of 26 cm2 and 11.7% on a module area of 204 cm2. The decisive developments leading to this achievement include the optimization of the module layout as well as the high‐resolution short‐pulse (nanosecond) laser structuring processes involved in the manufacturing of such modules. By minimizing the inactive areas within the total module area that are used for interconnecting the individual solar cells of the module in series, geometric fill factors of over 95% have been achieved. A production yield of 100% working modules during the manufacturing of these modules and an extremely narrow distribution of the final PCE values underline the excellent process control and reproducibility of the results. The new developments and their implementation into the production process of the record OPV modules are described in detail, along with the challenges that arose during this development. Finally, dark lock‐in thermography (DLIT), electroluminescence (EL), and photoluminescence (PL) measurements of the record module are presented.

The reversible device performance of organic solar cells is caused by light-induced long-persistent radicals, and can be released with activation energy provided by thermal annealing.

For new photovoltaic (PV) technologies the improvement and quantification of stability is a significant challenge. Accelerated aging tests are usually applied to shorten development times. While many of these tests exist, there is no standard that can serve as a guideline for choosing relevant tests and applying them correctly to achieve comparability between results. With the IEC TS 62876‐2‐1:2018 for the first time, a standard has been developed which defines the most significant testing protocols for stability. This standard is designed especially for third‐generation PV technologies that utilize nanomaterials but may be applied to any other new PV technologies as well. The standard defines the relevant stresses, recommends values for testing, defines seven standard tests, and provides reporting requirements for documentation. As a stability criterion the time t80, the time it takes for a measured efficiency of a device to reach 80% of the initial efficiency during the testing, is applied. A wide adoption of the tests outlined in the IEC TS 62876 significantly shortens development times and decreases the time to market for emerging PV technologies.

AbstractSolution‐processed bulk heterojunction organic photovoltaic (OPV) devices have gained serious attention during the last few years and are established as one of the leading next generation photovoltaic technologies for low cost power production. This article reviews the OPV development highlights of the last two decades, and summarizes the key milestones that have brought the technology to today’s efficiency performance of over 7%. An outlook is presented on what will be required to drive this young photovoltaic technology towards the next major milestone, a 10% power conversion efficiency, considered by many to represent the efficiency at which OPV can be adopted in wide‐spread applications. With first products already entering the market, sufficient lifetime for the intended application becomes more and more critical, and the status of OPV stability as well as the current understanding of degradation mechanisms will be reviewed in the second part of this article.

Nonradiative recombination reduces the open-circuit voltage relative to its theoretical limit and leads to reduced luminescence emission at a given excitation. Therefore, it is possible to correlat...

AbstractOver the last two decades the organic solar cell community has synthesized tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material. These materials are often crudely evaluated simply by measuring the current–voltage (JV) curves in the light to obtain power conversion efficiencies (PCEs). Materials with low PCEs are quickly disregarded in the search for higher efficiencies. More complex measurements such as frequency/time domain characterization that could explain why the material performed as it is often not performed as they are too time consuming/complex. This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress. Herein, a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone. This technique reduces the time to fully analyze a working cell from weeks to seconds and opens up the possibility of not only fully characterizing new devices as they are fabricated, but also data mining historical data sets for promising materials the community has overlooked.