9 research outputs found
An Interlaboratory Study on the Stability of All-Printable Hole Transport Material–Free Perovskite Solar Cells
No full textComparisons between different laboratories on long-term stability analyses of perovskite solar cells (PSCs) is still lacking in the literature. This work presents the results of an interlaboratory study conducted between five laboratories from four countries. Carbon-based PSCs are prepared by screen printing, encapsulated, and sent to different laboratories across Europe to assess their stability by the application of three ISOS aging protocols: (a) in the dark (ISOS-D), (b) under simulated sunlight (ISOS-L), and (c) outdoors (ISOS-O). Over 1000 h stability is reported for devices in the dark, both at room temperature and at 65 degrees C. Under continuous illumination at open circuit, cells survive only for few hours, although they recover after being stored in the dark. Better stability is observed for cells biased at maximum power point under illumination. Finally, devices operate in outdoors for 30 days, with minor degradation, in two different locations (Barcelona, Spain and Paola, Malta). The findings demonstrate that open-circuit conditions are too severe for stability assessment and that the diurnal variation of the photovoltaic parameters reveals performance to be strongly limited by the fill factor, in the central hours of the day, due to the high series resistance of the carbon electrode
Spray coated silver nanowires as transparent electrodes in OPVs for Building Integrated Photovoltaics applications
No full textThe application of spray coated silver nanowires (AgNWs) onto OPVs for building Integrated Photovoltaics (BIPVs) is demonstrated. By using AgNWs with PEDOT:PSS, a transparent conductive layer was demonstrated on top of an P3HT:PCBM active layer with a sheet resistance of 30 Ω/⎕ for 90% transparency. This has been applied to two separate configurations; semi-transparent OPVs for solar glazing applications and OPVs onto an opaque substrate, namely steel. For the latter, a novel technique to planarise the steel substrate with an intermediate layer is also presented, with a substantial decrease in surface roughness reported to ensure that the substrate is smooth enough to use for OPV fabrication. The use of SU-8 as an intermediate layer reduced the surface roughness to RA=10 nm, which is one of the lowest values reported to date, and was achieved on a low cost substrate (DC01 low carbon steel) using solution processing
Mixed dimension silver nanowires for solution processed, flexible, transparent and conducting electrodes with improved optical and physical properties
No full textThis is an author-created, un-copyedited version of an article accepted for publication in Flexible and Printed Electronics. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://doi.org/10.1088/2058-8585/aa6011.In this work, we present an alternative method for the spray coating of silver nanowires contact electrodes by employing a mixture of short and long nanowires. Mixed silver nanowires are found to give improve optical properties with 2-5% higher transparency for the same sheet resistance of 25 Ωsq-1, when compared to silver nanowires prepared with a single geometry nanowire. The figure of merit (FoM) for the 25 Ωsq-1 sheet resistance electrode was found to be highest for the mixed composition AgNWs-M1 based electrodes. Furthermore, the average root mean square surface roughness (Rq) parameter by WLI measurement are found to be lower for the mixed composition silver nanowires electrodes (Rq= 3-4 nm) when compare to the individual parent fixed dimension type silver nanowire electrodes (Rq = 6-8 nm)
Studying the outdoor performance of organic building-integrated photovoltaics laminated to the cladding of a building prototype
No full textThe outdoor dependence of module orientation and diurnal climatic conditions on the performance of Organic Photovoltaics (OPVs) configured for Building Integrated PV (BIPV) arrays is reported. The study focuses upon a Northern European climate and the significance of module orientation upon energy yield across diurnal, seasonal change and climatic conditions are discussed. It is shown that the optimum position of a BIPV facade depends upon season and that a south facing BIPV facade provides the greatest energy yield during winter months. The results also show how west-facing modules can significantly contribute to power generation during peak power periods (5–8 p.m.), which is imperative for balancing energy demand for buildings of the future and in particular supply the energy needs of buildings during peak hours in Northern Europe. Electrical characteristics under standard and part-load conditions were collated from laboratory scale OPV module experimental data and scaled for commercial-size modules in order to simulate BIPV arrays based upon OPVs. The simulated data is compared to experimental data and the closeness shows that BIPV systems based upon OPVs can be accurately simulated prior to installation. The system simulations compare typical energy demand profiles of small commercial buildings and illustrate that OPV arrays show strong potential to be used with excess energy generation for 8 months of the year based upon a 4.22kWp OPV system. Four 4.22kWp OPV systems scenarios have been investigated for (1) the highest annual energy generation, (2) architecturally evenly-spaced around the building (avoiding a North façade), (3) grid-balancing and (4) East-West split. Whilst Scenario 4 shows the lowest overall energy yield over the course of the year, energy production during peak hours is substantially higher than in other scenarios. The options presented show that OPVs are viable to use in BIPVs and can adequately meet the energy demand of a small commercial building during spring, summer and autumn in Norther Europe and can be adapted to end user's needs
Outdoor performance monitoring of perovskite solar cell mini-modules: Diurnal performance, observance of reversible degradation and variation with climatic performance
No full textThe outdoor performance monitoring of two types of perovskite solar cell (PSC) mini-modules based on two different absorbers - CH3NH3PbI3 (MAPI) and Cs0.05FA0.83MA0.17PbI(0.87Br0.13)3 (FMC) is reported. PSC modules displayed markedly different outdoor performance characteristics to other PV technologies owing to the reversible diurnal changes in efficiency, difference in temperature coefficient between absorber layers and response under low light conditions. Examination of diurnal performance parameters on a sunny day showed that whereas the FMC modules maintained their efficiency throughout the day, the MAPI modules peaked in performance during the morning and afternoon, with a strong decrease around midday. Overall, the MAPI modules showed a strongly negative temperature coefficient (TC) for PCE, whereas the FMC modules showed a moderate positive temperature coefficient performance as a function of temperature due to the increase in JSC and FF. Outdoor monitoring of the MAPI modules over several days highlighted that the reduced over the course of the day but recovered overnight. In contrast the FMC modules improved slightly during the daytime although this was too reversed overnight. This paper provides insight into how PSC modules perform under real-life conditions and consider some of the unique characteristics that are observed in this solar cell technology
Using ISOS consensus test protocols for development of quantitative life test models in ageing of organic solar cells
No full textAs Organic Photovoltaic (OPV) development matures, the demand grows for rapid characterisation of degradation and application of Quantitative Accelerated Life Tests (QALT) models to predict and improve reliability. To date, most accelerated testing on OPVs has been conducted using ISOS consensus standards. This paper identifies some of the problems in using and interpreting the results for predicting ageing based upon ISOS consensus standard test data. Design of Experiments (DOE) in conjunction with data from ISOS consensus standards are used as the basis for developing life test models for OPV modules. This is used to study their temperature-humidity and light-induced degradation, which enables failure rates during accelerated testing to be assessed against the typical outdoor operational conditions. The life test models are used to assess the relative severity of the ISOS standards and the impact of geographic and seasonal climatic changes on OPV degradation
Rapid evaluation of different perovskite absorber layers through the application of depth profile analysis using glow discharge – Time of flight mass spectrometry
No full textFinancial support from Horiba Scientific through a research project is gratefully acknowledged. Also, B.F. acknowledges her research contract RYC-2014-14985 to the Spanish Ministry of Economy and Competitiveness through the Ramón y Cajal Program. Authors thank Samuel Gonzalez for his help during the first PGD-ToFMS optimizations. J.K., P.T. and V.S. would like to acknowledge the European Regional Development Fund (ERDF) and the Welsh European Funding Office (WEFO) for funding the 2nd Solar Photovoltaic Academic Research Consortium (SPARCII) which supported this research and the Sêr Cymru National Research Network
High performing AgNW transparent conducting electrodes with a sheet resistance of 2.5 Ω Sq−1 based upon a roll-to-roll compatible post-processing technique
No full textThe report of transparent and conducting silver nanowires (AgNWs) that produce remarkable electrical performance, surface planarity and environmental stability is given. This research presents an innovative process that relies on three sequential steps, which are roll-to-roll (R2R) compatible; thermal embossing, infrared sintering and plasma treatment. This process leads to the demonstration of a conductive film with a sheet resistance of 2.5Ω/sq and high transmittance, thus demonstrating the highest reported figure-of-merit in AgNWs to date (FoM = 933). A further benefit of the process is that the surface roughness is substantially reduced compared to traditional AgNW processing techniques. Finally, consideration of the long-term stability is given by developing an accelerated life test process that simultaneously stresses the applied bias and temperature. Regression line fitting shows that a ∼150-times improvement in stability is achieved at ‘normal operational conditions’ when compared to traditionally deposited AgNW films. X-ray photoelectron spectroscopy (XPS) is used to understand the root cause of the improvement in long-term stability, which is related to reduced chemcial changes in the AgNWs
Classification of driver behavior using machine learning techniques and onboard monitoring with OBD ll in real road conditions
No full textLa movilidad vial y el buen comportamiento del conductor en la carretera es de vital importancia para mantener una movilidad sin accidentes de tránsito y conductores prudentes en las vías. Los sistemas inteligentes de transporte (SIT) brindan la optimización de la estructura vial incrementando el control, la eficiencia, efectividad, la educación de los conductores al momento de la conducción, con el objetivo de gestionar el crecimiento demanda de movilidad y el comportamiento de los conductores en las vías. Un aporte crucial para los sistemas inteligentes de transporte son las campañas de monitoreo en condiciones reales de carretera que permitan la recolección de datos y su vez identificar el tipo de comportamiento del conductor. En el proyecto desarrollado se implementó una campaña de monitoreo abordo con un dispositivo ODB ll instalado en una muestra de 5 vehículos, que por medio de la conexión a bluetooth y una App instalada en el Smartphone se realiza la captura de los datos pertinentes para identificar el comportamiento de conducción. Para la identificación de los comportamientos de conducción se desarrolló un modelo de Machine Learning por medio de la técnica K-Means donde se clasificaron a los conductores en 3 grandes grupos (clúster): conductor normal, agresivo y peligroso. Con la identificación de los comportamientos de conducción se logra evidenciar que el conductor peligroso al ir a velocidad altas, tiene un mayor consumo de combustible y el riesgo de ocasionar accidenten en la malla vial.INTRODUCCIÓN...............................................................................................13
1.MARCO TEÓRICO O ESTADO DEL ARTE.................................................16
1.1 MARCO TEÓRICO......................................................................................16
1.1.1 Comportamiento de conducción..............................................................16
1.1.2 Estilos de conducción...............................................................................22
1.2 ESTADO DEL ARTE ...................................................................................25
1.2.1 Análisis Bibliométrico ...............................................................................25
1.2.2 Tipos de comportamiento del conductor.................................................29
1.2.3 Instrumentación para la recolección de datos ........................................30
1.2.4 Técnicas de clasificación para el comportamiento del conductor .........31
2.METODOLOGÍA.............................................................................................33
3.MONITOREO DE VARIABLES DE OPERACIÓN Y ACTUALIZACIÓN
DE LA BASE DE DATOS...............................................................................34
3.1 CAMPAÑA DE MONITOREO.....................................................................34
3.1.1 Ruta Seleccionada ...................................................................................35
3.1.2 Datos técnicos de los vehículos monitoreados.......................................36
3.1.3 Datos sociodemográficos de los conductores ........................................37
3.1.4 Variables monitoreadas ...........................................................................38
3.1.5 Sistema de monitoreo ejecutado.............................................................39
3.1.6 Sistema de captura de los datos .............................................................40
3.1.7 Canal de Conectividad para él envió de la información.........................42
3.2. SISTEMA CAPTURAR DE DATOS...........................................................44
3.2.1 Almacenamiento de datos .......................................................................48
3.2.2 Captura de los datos ................................................................................50
3.2.3 Eliminación de Datos Atípicos .................................................................50
3.2.4 Registro de datos en la nube...................................................................54
3.3 BASE DE DATOS PROYECTO ACTUAL 2023 ........................................54
3.4 BASE DE DATOS CONCATENADA..........................................................56
4.TÉCNICA DE MACHINE LEARNING PARA LA CLASIFICACIÓN DE
LOS COMPORTAMIENTOS DE CONDUCCIÓN ........................................58
7
4.1 METODOLOGÍA APLICADA PARA LA CLASIFICACIÓN DE LOS
COMPORTAMIENTOS DE CONDUCCIÓN. ...................................................58
4.2 ELECCIÓN Y CONFIGURACIÓN DEL ENTORNO DE DESARROLLO .62
4.2.1 Entorno de desarrollo integrado IDE.......................................................62
4.2.2 Listado de IDE en el lenguaje de programación Python........................63
4.2.3 Cuadro comparativo de los IDE...............................................................64
4.3 CONSTRUCCIÓN DEL MODELO DE MACHINE LEARNING.................65
4.3.1 Paso a paso para la construcción del modelo de Machine Learning:...67
4.4 ANÁLISIS DE LOS DATOS ........................................................................70
4.5 MODELO DE MACHINE LEARNING.........................................................76
4.6 PREDICCIONES SEGÚN EL MODELO DE MACHINE LEARNING .......89
4.6.1 Pasos para realizar la predicción con el modelo de Machine Learning 90
4.7 RESULTADOS OBTENIDOS DE LAS PREDICCIONES DE LOS
CONDUCTORES...............................................................................................98
4.8 ANÁLISIS DE LOS DIAGRAMAS SAFD..................................................102
5.VALIDACIÓN DE RESULTADOS POR MEDIO DE GUI (INTERFAZ
GRÁFICA DE USUARIO) ............................................................................104
5.1 VALIDACIÓN DEL ALGORITMO .............................................................104
5.2 INTERFAZ GRÁFICA................................................................................109
5.2.1 Librerías implementadas en Python para la creación de la interfaz
gráfica…...........................................................................................................110
5.2.2 Proceso de construcción de la GUI.......................................................112
6.CONCLUSIONES.........................................................................................118
7.RECOMENDACIONES Y TRABAJOS FUTUROS ....................................119
REFERENCIAS Y BIBLIOGRAFIA.................................................................120
LISTA DE ANEXOS.........................................................................................126
ANEXOS..........................................................................................................127MaestríaRoad mobility and good driver behavior on the road is of vital importance to maintain mobility without traffic accidents and prudent drivers on the roads. Intelligent transportation systems (ITS) provide optimization of the road structure by increasing control, efficiency, effectiveness, and driver education at the time of driving, with the aim of managing the growing demand for mobility and the behavior of drivers. drivers on the roads. A crucial contribution to intelligent transportation systems are monitoring campaigns in real road conditions that allow data collection and in turn identify the type of driver behavior. In the developed project, an on-board monitoring campaign was implemented with an ODB II device installed in a sample of 5 vehicles, which through a Bluetooth connection and an App installed on the Smartphone captures the relevant data to identify the driving behavior. To identify driving behaviors, a Machine Learning model was developed using the K-Means technique where drivers were classified into 3 large groups (cluster): normal, aggressive and dangerous driver. With the identification of driving behaviors, it is possible to show that the dangerous driver, when traveling at high speed, has greater fuel consumption and the risk of causing accidents on the road network.Modalidad Virtua
