Global ETD Search

311	EXPLORING THE POTENTIAL OF LOW-COST PEROVSKITE CELLS AND IMPROVED MODULE RELIABILITY TO REDUCE LEVELIZED COST OF ELECTRICITY Reza Asadpour (9525959) 16 December 2020 (has links) <div>The manufacturing cost of solar cells along with their efficiency and reliability define the levelized cost of electricity (LCOE). One needs to reduce LCOE to make solar cells cost competitive compared to other sources of electricity. After a sustained decrease since 2001 the manufacturing cost of the dominant photovoltaic technology based on c-Si solar cells has recently reached a plateau. Further reduction in LCOE is only possible by increasing the efficiency and/or reliability of c-Si cells. Among alternate technologies, organic photovoltaics (OPV) has reduced manufacturing cost, but they do not offer any LCOE gain because their lifetime and efficiency are significantly lower than c-Si. Recently, perovskite solar cells have showed promising results in terms of both cost and efficiency, but their reliability/stability is still a concern and the physical origin of the efficiency gain is not fully understood.</div><div><br></div>In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.<br> Solar Cell Solar Module Reliability Levelized Cost of Electricity LCOE Perovskite Solar Cells Ruddlesden-Popper Perovskite Tandem Solar Cell Bifaicial Solar Cell Contact Corrosion Contact Delamination Solder Bond Failure Dark Lock-in Thermography DLIT Markov Chain Method
312	Microstructural Investigations of Low Temperature Joining of Q&P Steels Using Ag Nanoparticles in Combination with Sn and SnAg as Activating Material Hausner, Susann, Wagner, Martin Franz-Xaver, Wagner, Guntram 14 February 2019 (has links) Quenching and partitioning (Q&P) steels show a good balance between strength and ductility due to a special heat treatment that allows to adjust a microstructure of martensite with a fraction of stabilized retained austenite. The final heat treatment step is performed at low temperatures. Therefore, joining of Q&P steels is a big challenge. On the one hand, a low joining temperature is necessary in order not to influence the adjusted microstructure; on the other hand, high joint strengths are required. In this study, joining of Q&P steels with Ag nanoparticles is investigated. Due to the nano-effect, high-strength and temperature-resistant joints can be produced at low temperatures with nanoparticles, which meets the contradictory requirements for joining of Q&P steels. In addition to the Ag nanoparticles, activating materials (SnAg and Sn) are used at the interface to achieve an improved bonding to the steel substrate. The results show that the activating materials play an important role in the successful formation of joints. Only with the activating materials, can joints be produced. Due to the low joining temperature (max. 237 °C), the microstructure of the Q&P steel is hardly influenced. info:eu-repo/classification/ddc/620 ddc:620
313	Prognostics for Condition Based Maintenance of Electrical Control Units Using On-Board Sensors and Machine Learning Fredriksson, Gabriel January 2022 (has links) In this thesis it has been studied how operational and workshop data can be used to improve the handling of field quality (FQ) issues for electronic units. This was done by analysing how failure rates can be predicted, how failure mechanisms can be detected and how data-based lifetime models could be developed. The work has been done on an electronic control unit (ECU) that has been subject to a field quality (FQ) issue, determining thermomechanical stress on the solder joints of the BGAs (Ball Grid Array) on the PCBAs (Printed circuit board assembly) to be the main cause of failure. The project is divided into two parts. Part one, "PCBA" where a laboratory study on the effects of thermomechanical cycling on solder joints for different electrical components of the PCBAs are investigated. The second part, "ECU" is the main part of the project investigating data-driven solutions using operational and workshop history data. The results from part one show that the Weibull distribution commonly used to predict lifetimes of electrical components, work well to describe the laboratory results but also that non parametric methods such as kernel distribution can give good results. In part two when Weibull together with Gamma and Normal distributions were tested on the real ECU (electronic control unit) data, it is shown that none of them describe the data well. However, when random forest is used to develop data-based models most of the ECU lifetimes of a separate test dataset can be correctly predicted within a half a year margin. Further using random survival forest it was possible to produce a model with just 0.06 in (OOB) prediction error. This shows that machine learning methods could potentially be used in the purpose of condition based maintenance for ECUs. machine learning random forest random survival forest condition based maintenance cbm reliability solder joint failure thermomechanical cycling ECU lifetime prediction data-driven statistics BGA PCBA field quality maintenance truck bus vehicle Probability Theory and Statistics Sannolikhetsteori och statistik Transport Systems and Logistics Transportteknik och logistik Robotics Robotteknik och automation Control Engineering Reglerteknik
314	Beyond the dichotomies of a coercion and voluntary recruitment Afghan unaccompanied minors unveil their recruitment process in Iran Rami, Ali January 2018 (has links) No description available. Child solder Syria Iran Afghanistan Sweden Refugee asylum seeker War coercion voluntary recruitment Shia Shiite Fatemiyoun Brigade Migration Hazara Socio-economic injustice Minors Barnsoldat Syrien Iran Afghanistan asyl ensamkommande krig Fatemiyoun Brigad Sverige Migrationsverket Hazara الإكراه التجنيد الطوعي الجنود الأطفال سوريا إيران أفغانستان الهزارة لواء الفاطميون الشيعة الحرب Philosophy Filosofi Cultural Studies Kulturstudier Other Humanities not elsewhere specified Övrig annan humaniora Övrig annan samhällsvetenskap

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