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441 results for “Battery”
Supporting data for "Towards reliable three-electrode cells for lithium–sulfur batteries"
<p>This is the dataset of electrochemical measurements for our publication "Towards reliable three-electrode cells for lithium–sulfur batteries". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p>Abstract of the manuscript:</p> <p>Three-electrode measurements are valuable to the understanding of the electrochemical processes in a battery system. However, their application in lithium–sulfur chemistry is difficult due to the complexity of the system and thus rarely reported. Here, we present a simple three-electrode cell format with relatively good life time and minimum interference with the cell operation. </p>
Underling data for "The Battery Life Estimation of a Battery under Different Stress Conditions"
<p>Each file reports the capacity trend as a function of the number of cycles, for NMC cells aged according to different protocols, as reported in the following table:</p> <table> <tbody> <tr> <td> <p><strong>Test Number</strong></p> </td> <td> <p><strong>Discharge Current (C-Rate)</strong></p> </td> <td> <p><strong>ΔSOC = SOCin − SOCfin</strong></p> </td> <td> <p><strong>Cycle between Control Tests</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>2 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100 <sup>1</sup></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>1 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>1 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>5 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>3 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160 </p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>8 × 3C@80%DOD + 10 × 2C@40%DOD</p> </td> <td> <p>90−10, 70−30</p> </td> <td> <p>90</p> </td> </tr> </tbody> </table> <p>The number of the file corrrespond to the test number (e.g. B1 referers to results for a cell undergone test 1). Cells tested where 20 Ah EIG NMC-graphite.</p> <p>In order to compare the results for cells subjected to life tests with different ΔSOCs, it is necessary to appropriately quantize the cumulative charge, to obtain a suitably defined equivalent cycle. To this end, we consider the greatest common factor (GCF) among ΔSOCs. We then define the number of equivalent cycles (ECs) of each life test as the ratio between the ΔSOC value and GCF. In our case, we considered 3 ΔSOC: 40%, 60%, and 80%. The GCF is therefore 20%. Hence, the number of EC equals 2, 3, and 4 for each cycle at ΔSOC 40, 60, and 80%, respectively. Obviously, this is not the only quantization possible, but a different choice does not affect the validity of the model, as long as the transformation is linear, although it would lead to different results for the parameters.</p> <p>The capacity has been evaluated with a standard charge-discharge cycle at 0.5C rate, after a given number of test cycles, as reported in the files. All files are structured as follows:</p> <table> <tbody> <tr> <td>Column number</td> <td>1</td> <td>2</td> <td>3</td> </tr> <tr> <td>Field description</td> <td>Number of cycles</td> <td>Number of equivalrent cycles</td> <td>Relative capacity</td> </tr> </tbody> </table> <p>Relative capacity: ratio between the capacity value after N cycle and the capacity value at the beginning of life.</p>
Supplementary data: Na2.4Al0.4Mn2.6O7 anionic redox cathode material for sodium ion batteries- a combined experimental and theoretical approach to elucidate its charge storage mechanism
<p>This data repository contains the output files of density-functional theory (DFT) calculations that were used for the paper "Na2.4Al0.4Mn2.6O7 anionic redox cathode material for sodium ion batteries- a combined experimental and theoretical approach to elucidate its charge storage mechanism". </p>
In-operando visualization of redox flow battery in membrane-free microfluidic platform
<p>Images are raw data of main figure 1.(B-D) from "In-operando visualization of redox flow battery in membrane-free microfluidic platform"</p>
Dataset for the paper: "Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance"
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Andres Parra-Puerto, Javier Rubio-Garcia, Matthew Markiewicz, Zhuo Zheng and Anthony Kucernak</p> <p>Carbon Aerogel Based Thin Electrodes for Zero-Gap all Vanadium Redox Flow Batteries – Quantifying the Factors Leading to Optimum Performance </p> <p>DOI: https://doi.org/10.1002/celc.202101617 </p> <p>Please cite the above reference if you wish to use this data </p> <p>DOI of this data file is: 10.5281/zenodo.6261512</p>
Monitoring data second-life battery at Cloverleaf
<p>Dataset consisting of monitoring data on the second-life battery system that was implemented in the context of Circusol:</p> <p>- Round Trip Efficiency<br> - State of Health evolution<br> - Typical Day V I & Vcell</p>
Monitoring data second-life battery Cloverleaf demonstrator Circusol
<p>This dataset includes monitoring data on the second-life battery system that was implemented in the Cloverleaf demonstrator in the context of Circusol.</p> <p>The following data are included:<br> - State of Health evolution<br> - Round trip Efficiency<br> - Typical day V I<br> - Typical day Vcell</p>
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
<p>Here are the datasets for the publication named "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation" published in Nature Communications. Experimental cycling data for three commercial 18650 type batteries (Dataset_1:NCA battery, Dataset_2:NCM battery, and Dataset_3:NCM+NCA_battery) are given, where each csv file corresponds to one cell cycling data. The cells are named as CY<em>X-Y_Z</em>-#<em>N</em> according to their cycling conditions. <em>X</em> means the temperature, <em>Y_Z</em> represents the charge_discharge current rate, #<em>N</em> is the cell tag. Each csv file has 9 columns, including cycle time ('time/s'), controlled voltage and current ('control/V/mA'), battery voltage ('Ecell/V'), applied current ('<I>/mA'), charge or discharge electricity ('Q discharge/mA.h' and 'Q discharge/mA.h'), controlled voltage or current ('control/V', 'control/mA' and ), and cycle number ('cycle number'). In the impedance data, one representative cell from each cycling condition is chosen for the discussion in the main text. More detailed descriptions can be found in the zip file.</p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
Research data: "Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe"
<p>This data set belongs to the paper Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe in the International Journal of Production Economics (DOI). The BatPac model, as well as, the model for the economic assessment of the recycling route are not included. The needed data can be found in the file (name). Further, the BatPaC model can be gather from the website of Argonne National Laboratory and the assessment tool for the recycling routes via this DOI: 10.5281/zenodo.6500946.</p> <p> </p> <p>This work is part of the research project Recycling 4.0 (EFRE | ZW 6-85018080), which is funded by the European Regional Development Fund and managed by the development bank for the German federal state of Lower Saxony (NBank).</p>
Experimental Calendar Ageing Data for Lithium-Ion Battery Chemistries
<p>This data set has been generated by Technische Hochschule Ingolstadt (THI). If there are any questions,<br> please do not hesitate to get in touch with the authors.</p>
Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
<p>In this folder, excel files are stored with the results of signal processing that supported findings in the following paper:</p> <p>"Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steell battery tabs".</p> <p>Matlab scripts and orginal signals will be uploaded soon with more detailed description.</p> <p> </p>
Next-generation batteries - Moniek Tromp - In Science #35 - RUG Podcast
<p>In Science RUG Podcast.</p> <p>Our guest today is Moniek Tromp. She’s a Professor of Materials Chemistry at the Faculty of Science and Engineering and part of the BatteryNL consortium, which received a large NWA grant earlier this year. Wim, Tina and Arjen interview her about her research on catalysis and improving batteries & fuel cells. Batteries will play a vital role in storing renewable energy and creating a more sustainable society.</p> <p>Guest: Professor <a href="https://twitter.com/moniek_tromp">Moniek Tromp</a></p> <p>Hosts: <a href="https://twitter.com/wimbrons">Wim Brons</a>, <a href="https://twitter.com/DocTinaK">Tina Kretschmer</a> and <a href="https://twitter.com/afbdijkstra">Arjen Dijkstra</a></p>
Dataset related to the publication "New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries"
<p>The formation of a protecting nano-layer, so-called Solid Electrolyte Interphase (SEI), on the negative electrode of Li-ion batteries (LIBs) from product precipitation of the cathodic decomposition of the electrolyte is a blessing since the electrically-insulating nature of this nano-layer protect the electrode surface preventing continuous electrolyte decomposition and enabling the large nominal cell voltage of LIBs, e.g. 3.3 – 3.8 V. Thus, the protecting performance of the nano-layer SEI is essential for LIBs to achieve long cycle life. Unfortunately, evaluation of this critical property of the SEI is not trivial. Herein, a new, cheap and easily-implementable methodology is presented to estimate the protecting quality of the SEI; the redox-mediated enhanced coulometry. The key element of the methodology is the addition of a redox-mediator in the electrolyte during degassing step (after the SEI formation cycle). The redox-mediator leads to an internal self-discharge process that is inversely proportional to the protecting character of the SEI. And the self-discharge process results in an easily-measurable decrease in coulombic efficiency. The influence of vinylene carbonate as electrolyte additive in the resulting SEI is used as case study to showcase the potential of the proposed methodology</p>
Batteries of Leiden jars
The battery consists of 9 Leiden jars. They are all closed with wooden lids, in which brass rods ended with knobs are mounted. Each of them is connected to the other ones by means of brass rods. The Leiden jar is the oldest form of a capacitor, a device used to store electrical charges. The jars connected in a battery allowed for increasing the amount of the charge stored. This device was independently invented in 1745 by Pieter van Musschenbroek (1692–1761), a professor at Leiden University (hence the name of the device), and Ewald Jürgen von Kleist (1700–1748), a lawyer and scholar working in Kamień Pomorski. The jar could be electrically charged by contacting the rod with an energised body. Time and place of creation: 19th century, England Inventory number: 17072; 2187/V Museum: Jagiellonian University Museum Collegium Maius https://muzea.malopolska.pl/en/objects-list/2765 Digitalisation: Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Batterie De La Cride
Rendu photogrammétrique du fort de la pointe de la Cride à Sanary sur mer, Source: Objaverse 1.0 / Sketchfab
WW1 artillery battery, Helsinki, Finland
First World War artillery battery 65 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Alakivenpuisto, Myllypuro, Helsinki, Finland. The battery was built in 1915. Battery has four U shaped positions for mortars. Two of them still contains mortar emplacement. Every position has two ammunitions cabinets in concrete walls. Every position has shelter room inside concrete wall. Entrances to these shelters have been sealed later on. More info: * [Location in Google Maps](https://goo.gl/maps/zjfghd6FfEjoysTx9) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000011940) (only in finnish) Source: Objaverse 1.0 / Sketchfab
WW1 artillery battery outline, Helsinki, Finland
First World War artillery battery in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) naval front defense line in Skatanniemi, Vuosaari, Helsinki, Finland. The battery was built in 1916 - 1918. The battery has two identical parts: western and eastern. This model is from the eastern part. The western part is in [another model](https://skfb.ly/6SS98). The eastern part of the battery contains two mortar positions, ammunition cellar and shelter room. The roof is missing from the model. More info: * [Location in Google Maps](https://goo.gl/maps/tsRLpmA4y6dybHk29) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000007645) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Mugdock AA Battery - Aerial 3D Model
An aerial 3D model, with my first use of a drone, of the WWII Anti Aircraft Battery at Mugdock Country Park just to the north of Milngavie. The battery consists of four gun pits and a control bunker. The battery was part of a series of Anti Aircraft Defences which were constructed after the Clydebank Blitz of 1941. For more information on the site check out the Canmore entry https://canmore.org.uk/site/105603/mugdock-wood-battery Source: Objaverse 1.0 / Sketchfab
WW1 artillery battery shelter, Helsinki, Finland
First World War artillery battery shelter room in battery 72 in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line in Tattariharju, Helsinki, Finland. The base was built in 1915 - 1918. The room walls were originally covered with logs. Battery 72 is part of defense base IX:12. More info: * [Location in Google Maps](https://goo.gl/maps/tv5qnNCHMBW3yGGS8) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013411) (only in finnish) Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.