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441 results for “Battery”
Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition (Data)
<p>Figures and data files used to construct the figures in the 2022 paper "Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition."</p> <p> </p>
Supporting Data for: Resolving high potential structural deterioration in Ni-rich layered cathode materials for lithium-ion batteries operando
<p>The following file contains supporting data for "Resolving high potential structural deterioration in Ni-rich layered cathode materials for lithium-ion batteries operando" manuscript.</p>
QENS model of Battery data
<p>QENS modeling of diffusion coefficients of Li-Ion batteries during the PaNOSC Summer School 2022 to get to know some things about QENS and FAIR sharing of data.</p> <p>Modeled QUENS for diffusion coefficients of the pristine anodes of different battery cells, given in <a href="https://doi.org/10.1149/2.0301805jes">https://doi.org/10.1149/2.0301805jes</a></p>
Data access - Operando characterization and theoretical modelling of metal|electrolyte interphase growth kinetics in solid-state-batteries - part I: experiments
<p>The zip file contains XPS and EIS data used in parts 1 and 2 of the publication entitled: "New insights into the kinetics of metal|electrolyte interphase growth in solid-state-batteries via an <em>operando</em> XPS analysis"</p> <p>Folders description: </p> <p>- "XPS" contains three subfolders with the XPS data and fitting models (in .vms format, CasaXPS) corresponding to the reference Na metal sample, the Na|NZSPas interface and Na|NZSPpolished interface</p> <p>- "EIS" contains two subfolders with the EIS data from the Na|NZSPas and Na|NZSPpolished symmetrical cells. The raw data is stored as .mpr files (EC-lab), and the fitted data is stored as .eis3 files (RelaxIS)</p>
Dataset of 5035 Conductivity Experiments for Lithium-Ion Battery Electrolyte Formulations at Various Temperatures
<p>Dataset containing 5035 Conductivity Experiments for Lithium-Ion Battery Electrolyte Formulations at Various Temperatures which is accopaning the data descriptor publication titled "5035 Conductivity Experiments for Lithium-Ion Battery Electrolyte Formulations at Various Temperatures and their Automated Analysis" by the same authors.</p>
Dataset: Experimental Investigation of Power Available in Lithium-Ion Batteries
<p>Dataset for the paper Experimental Investigation of Power Available in Lithium-Ion Batteries. </p> <p>The paper is available (open access) at <a href="https://doi.org/10.1016/j.jpowsour.2024.235168">https://doi.org/10.1016/j.jpowsour.2024.235168</a> </p> <ul> <li>Battery: Melasta LCO 6.8Ah pouch cell</li> <li>Test: Dynamic load profile followed by a 30-second 10C rate pulse</li> <li>Experiment I: Tests @25°C, 05 fresh cells, 05 repetitions on each cell</li> <li>Experiment II: One-factor-at-time experiment, [50°C, 15°C, 20kPa, 60kPa, 100% SOC, 20% SOC]</li> </ul> <p> </p>
3D lithium-ion battery image for testing P3T-Net
<p>These are the dataset used for training and testing P3T-Net for 3D unpaired domain transfer in .tif format. Images can be directly opened with ImageJ, Avizo, Python, Matlab, etc.</p> <p>Target domain: nano-CT image of a dual-mode scan of Lithium-ion battery cathode (voxel size: 128nm); Source domain: a nano-CT images of a single-mode scan of lithium-ion battery cathode (voxel size: 128nm).</p>
Research data for journal article: Balancing Environmental and Economic Impacts in the rapidly evolving European EV Battery Value Chain
Open the record for dataset details and reuse information.
Videos of electrochemiluminescence response for paper "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries"
<p>Videos showing the real time electrochemiluminescent light production in a serpentine flow field of a redox flow battery type system. The two videos show the difference when only an ITO electrode is used compared to when an ITO and carbon paper electrode is used.</p>
Data file for paper: Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries, Electrochemistry Communications, 2018
<p>Excel Data file containing the data presented in the figures of the paper:</p> <p>Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries</p> <p>Electrochemistry Communications, 2018,</p> <p>DOI:10.1016/j.elecom.2018.07.002</p> <p>Please cite the above reference if you wish to use this data</p>
Text-fig. 10. The lower incisor-canine battery of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Gips 6 (A1 – oblique stereo anterior view, A2 – anterior view); B) SMNK Ma1 Gips 17, oblique stereo anterior view (scales 10 mm). in Hippopotamodon erymanthius (Suidae, Mammalia) from Mahmutgazi, Denizli-Çal basin, Turkey
Text-fig. 10. The lower incisor-canine battery of Hippopotamodon erymanthius from Mahmutgazi, Turkey. A) SMNK Ma1 Gips 6 (A1 – oblique stereo anterior view, A2 – anterior view); B) SMNK Ma1 Gips 17, oblique stereo anterior view (scales 10 mm).
Dataset for optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries
<p>Data outputs from optimisation of Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries.</p> <p>Input data and code can be found here:</p> <p>https://github.com/PyPSA/WHOBS</p> <p>The summary of the results with metadata can be found here:</p> <p>https://github.com/PyPSA/WHOBS/tree/master/results-181002</p> <p>if you don't want to download 6 GB.</p> <p>To load the data, install PyPSA and do e.g. for Germany (DE) in 2030:</p> <p>network = pypsa.Network("DE-2030.nc")</p> <p>Note that in these files, in some hours batteries discharge and charge at the same time. This is because the model wants to dump energy and this behaviour has the same cost and effect as curtailing wind and solar. In the latest version of the github code, wind and solar have tiny marginal costs (0.02 and 0.01 EUR/MWh respectively) to prevent this behaviour. With these costs, curtailment is preferred.</p>
Data file for paper: Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018</p> <p>DOI: 10.1088/2515-7655/aaee17 </p> <p>Please cite the above reference if you wish to use this data</p>
Is lithium from geothermal brines the sustainable solution for Li-ion batteries?
<p>The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li <a title="Learn more about ore deposits from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/ore-deposit">ore deposits</a> such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering <a title="Learn more about life cycle assessments from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/life-cycle-assessment">life cycle assessments</a>. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. <a title="Learn more about Climate change impacts from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/climate-change-impact">Climate change impacts</a> of selected cases vary within 18–59 kg CO<sub>2</sub>eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO<sub>2</sub>eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO<sub>2</sub>eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of <a title="Learn more about fossil from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/fossil">fossil</a> energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.</p>
Dataset of "Wasting of Critical Raw Materials in Batteries for Single-Use E-Cigarettes "
<p>This paper focuses on the use of lithium-ion batteries in single-use e-cigarettes, examining their material composition and impact on the waste of critical raw materials. The consumption of these single-use e-cigarettes, which employ lithium-ion cells with a potential lifespan of several hundred cycles if reused, leads to the waste of critical and strategic raw materials in the Czech Republic alone. These discarded materials could have been used to manufacture several thousand batterypacks for electric vehicles annually.</p>
Supporting Data for "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems"
<p>This is the dataset of the data for our publication "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems". This archive contains all the raw data gathered and used to produce the results presented in this manuscript.</p> <p>Abstract of manuscript:</p> <div> <div> <div> <div> <p>Potassium-ion batteries (KIBs) are a promising alternative to conventional lithium- ion batteries with reduced critical mineral dependency, but accurate three-electrode characterization is hindered by the lack of a suitable reference electrode. Potassium metal is frequently used as a reference electrode out of necessity, but its high reactivity and unstable potential limit its reliability. Here we investigate the K-In and K-Bi alloy systems, synthesize two-phase In-In4K and Bi-Bi2K alloys, and identify Bi-Bi2K as a promising material owing to its stable potential of 1.07 V vs. K+/K. We prove the use of Bi-Bi2K as a reference electrode by cycling graphite in three-electrode cells and demonstrate that it results in significantly less electrolyte reduction than potassium metal, facilitating the accurate electrochemical characterization necessary to accelerate KIB development.</p> </div> </div> </div> </div>
Non-fluorinated electrolytes with micelle-like solvation for ultrahigh energy density lithium metal batteries
<p>Electrolyte engineering plays a critical role in enabling lithium (Li) metal batteries. However, the simultaneous realization of anion-rich solvation structure and high ionic conductivity of electrolytes via solvation structure design remains challenging. Here, we report a low-cost, non-fluorinated electrolyte with a micelle-like solvation structure by introducing amphiphilic n-butyl methyl ether (MNBE) into lithium bis(fluorosulfonyl)imide (LiFSI)/1,2-dimethoxyethane (DME) for stable Li metal batteries. MNBE can effectively promote Li+-FSI- coordination through steric crowding. Meanwhile, the inert alkyl chains of MNBE can mitigate the reaction between electrolyte and Li metal due to their lithiophobicity. Specifically, the micelle-like, non-fluorinated electrolyte exhibits an ionic conductivity as high as 12.55 mS cm-1 and its anion-rich solvation structure promotes the formation of LiF-rich solid-electrolyte-interphase. We constructed a 7.3 Ah Li||NMC811 pouch cell employing this electrolyte under harsh conditions, exhibiting ultrahigh specific energy of 503.7 Wh kg-1 with impressive cycling stability of 84.1% capacity retention after 100 cycles. </p>
Feature Engineered Dataset from HPPC Files for Lithium-Ion Battery Cells
<p>This dataset contains time-series data from Hybrid Pulse Power Cycle (HPPC) tests of lithium-ion battery cells, specifically designed for evaluating cell performance and degradation over multiple cycles. The dataset originally published by (Popp et al., 2024) includes information from both charge and discharge tests for each cell, with additional engineered features derived from the raw data. The cells in this dataset were evaluated under different States of Health (SoH), providing insights into performance across various degradation levels.</p> <h2>Dataset Overview</h2> <p>The dataset comprises charge and discharge cycles from 256 individual cells. Each test involves the following procedures:</p> <ol> <li>Cells were equilibrated at 25 °C inside a thermal chamber to ensure a stable temperature before testing.</li> <li>Cells were charged using constant current/constant voltage (CC/CV) mode with a charging rate of C/2 and a cutoff current of 98 mA.</li> <li>After a resting period to allow thermal stabilization, the cells were discharged at a rate of C/5 until the voltage dropped to 2.5 V, capturing the full discharge capacity.</li> <li>A second resting period was conducted until the cells reached thermal equilibrium.</li> <li>HPPC cycles were performed at multiple stages of the charge and discharge cycles to assess cell performance at different State of Charge (SOC) levels.</li> </ol> <p>The dataset also includes a micro HPPC cycle performed at every 10% SOC decrement, starting from 100% SOC down to 10% SOC, with a final HPPC test at 0% SOC. Cells were tested using a combination of charge and discharge pulses to simulate real-world usage patterns. All cycles were recorded at 100 Hz for standard charge/discharge cycles and 1 kHz for the HPPC tests, allowing for high-resolution analysis of the cell behavior.</p> <h2>Data Features</h2> <p>The dataset includes the following features recorded by (Popp. et al., 2024) during the tests:</p> <ul> <li><strong>Time</strong>: Timestamp for each data point.</li> <li><strong>ClimaTemp</strong>: Temperature recorded from the climate chamber.</li> <li><strong>I</strong>: Current applied to the cell (in Amps).</li> <li><strong>Itarget</strong>: Setpoint for the applied current (in Amps).</li> <li><strong>P</strong>: Power output of the cell (in Watts).</li> <li><strong>Q</strong>: Total charge accumulated in the cell (in Amp-seconds).</li> <li><strong>Qneg</strong>: Negative charge accumulated (in Amp-seconds).</li> <li><strong>Qpos</strong>: Positive charge accumulated (in Amp-seconds).</li> <li><strong>Temp_Cell</strong>: Temperature measured at the cell (in °C).</li> <li><strong>U</strong>: Cell voltage (in Volts).</li> </ul> <p>In addition to these core features, engineered features have been included to facilitate analysis of battery performance and degradation trends:</p> <ul> <li><strong>Cumulative_Cycles</strong>: Running count of cycles completed by the cell.</li> <li><strong>Avg_Voltage</strong>: The average voltage of the cell up to the current cycle.</li> <li><strong>Capacity_Fade_Rate</strong>: The rate of capacity degradation over time.</li> <li><strong>Avg_Temperature</strong>: Average temperature experienced by the cell up to the current cycle.</li> <li><strong>Temp_Variation</strong>: Maximum difference in cell temperature over time.</li> <li><strong>High_Temp_Flag</strong>: Binary flag indicating whether the cell temperature exceeded 40°C.</li> <li><strong>Internal_Resistance</strong>: Internal resistance of the cell, calculated from voltage and current data.</li> <li><strong>Power_Consumption_Rate</strong>: Rate at which power is consumed by the cell.</li> <li><strong>Energy_Efficiency</strong>: Efficiency of energy storage, calculated from the ratio of positive to negative charge.</li> <li><strong>Rolling_Avg_Voltage</strong>: Rolling average of the cell voltage over recent cycles.</li> <li><strong>Std_Dev_Voltage</strong>: Standard deviation of cell voltage across cycles.</li> <li><strong>Max_Voltage</strong>: Maximum voltage observed during the test.</li> <li><strong>Min_Voltage</strong>: Minimum voltage observed during the test.</li> <li><strong>Dynamic_Resistance</strong>: Resistance change calculated from voltage and current differentials.</li> <li><strong>Impedance</strong>: Impedance calculated from voltage and current changes.</li> <li><strong>Temp_Coefficient</strong>: Rate of change in power with respect to temperature.</li> <li><strong>Thermal_Runaway_Risk</strong>: Flag indicating the potential for thermal runaway conditions.</li> <li><strong>Effective_Capacity</strong>: Net charge retained by the cell after each cycle.</li> <li><strong>Energy_Throughput</strong>: Total energy delivered by the cell over time, calculated from power and time.</li> </ul> <p>This dataset is suitable for research into lithium-ion battery degradation, performance modeling, and State of Health prediction. The engineered features provide a robust foundation for predictive modeling, including the estimation of Remaining Useful Life (RUL) and optimization of battery usage in practical applications.</p> <p>The dataset has been structured for easy integration with machine learning workflows, with all features provided in CSV format for each test cycle.</p> <h3>Citation:</h3> <p>Popp, A., Spaeth, U., & Schmuelling, B. (2024). Samsung INR21700-50E Capacity and HPPC tests (V1.0) [Data set]. 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Rosemont, IL, USA. Zenodo. <a href="https://doi.org/10.5281/zenodo.10891871" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.10891871</a></p>
European Lithium Battery Recycling Facilities 2010-2030
<p>This dataset provides a comprehensive mapping of existing and announced lithium-ion battery recycling plants in Europe for the period 2010–2030. The data includes facilities identified through sources such as <a href="https://battery-news.de/en/europe-battery-recycling/">Battery News</a>, various news websites, and scientific literature. The format follows the <a href="../records/5708456">Swave dataset</a> and contains detailed information about each facility, including the company and other stakeholders involved, the location (country and city), operational status (operational, planned, or stopped), the recycling processes implemented, feedstock types, output products, and annual capacities. Additionally, it specifies, to the best of the author's knowledge, the elements and components being recovered at each facility.</p> <p><strong>Disclaimer</strong>: If you identify any incorrect information, please contact the author so the dataset can be promptly updated.</p>
Investigation of the influence of active cooling on battery cell testing - Measurement Data
<p>Measurement Data for Investigation of the influence of active cooling on battery cell testing.</p> <p>A detailled description of the tests and the arrangement of the numbered cells (see filenames) is given in the report, chapter II: Experimental.</p>
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.