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414 results for “Lithium”
Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset]
<p>Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies can help to better understand and model degradation and to optimize the operation strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications.<br>To our knowledge, the dataset presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells. The dataset includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution).<br>The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operation strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.</p>
Climate change and lithium mining influence flamingo abundance in the Lithium Triangle
<p><span>The development of technologies to slow climate change has been identified as a global imperative. Nonetheless, such 'green' technologies can potentially have negative impacts on biodiversity. We explored how climate change and the mining of lithium for green technologies influence surface water availability, primary productivity, and the abundance of three threatened and economically important flamingo species in the 'Lithium Triangle' of the Chilean Andes. We combined climate and primary productivity data with remotely sensed measures of surface water levels and a 30-year dataset on flamingo abundance using structural equation modeling. We found that, regionally, flamingo abundance fluctuated dramatically from year-to-year in response to variation in surface water levels and primary productivity but did not exhibit any temporal trends. Locally, in the Salar de Atacama — where lithium mining is focused — we found that mining was negatively correlated with the abundance of two of the three flamingo species. These results suggest continued increases in lithium mining and declines in surface water could soon have dramatic effects on flamingo abundance across their range. Efforts to slow the expansion of mining and the impacts of climate change are therefore urgently needed to benefit local biodiversity and the local human economy that depends on it.</span></p>
Supporting data for "Impact of compression on the electrochemical performance of the sulfur/carbon composite electrode in lithium–sulfur batteries"
<p>This is the dataset of electrochemical and operando X-ray diffracytion experiments for our publication "Impact of compression on the electrochemical performance of the sulfur/carbon composite electrode in 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 for the manuscript:</p> <p>While lithium–sulfur batteries theoretically have both high gravimetric specific energy and volumetric energy density, only its specific energy has been experimentally demonstrated to surpass that of the state-of-the-art lithium-ion systems at cell level. One major reason for the unrealized energy density is the low capacity density of the highly porous sulfur/carbon composite as the positive electrode. In this work, mechanical compression at elevated temperature is demonstrated to be an effective method to increase the capacity density of the electrode by at least 90% and moreover extends its cycle life. Distinct impacts of compression on the resistance profiles of electrodes with different thickness are investigated by tortuosity factors derived from both electrochemical impedance spectroscopy, X-ray computed tomography and kinetic analysis based on operando X-ray diffraction. The results highlights the importance of a homogeneous electrode structure for the lithium–sulfur system.</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>
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>
The composite metamorphic sequence in the Jiajika gneiss dome, Songpan-Ganze orogenic belt, eastern Tibet: P–T–D–t evolution and implications for lithium mineralization
<p>This is the dataset for "The composite metamorphic sequence in the Jiajika gneiss dome, Songpan-Ganze orogenic belt, eastern Tibet: P–T–D–t evolution and implications for lithium mineralization". The dataset includes EMPA, whole-rock major elements, and geochronological data.</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>
Supporting data: Ultraviolet astronomical spectrograph calibration with laser frequency combs from nanophotonic lithium niobate waveguides
<p>Data and code to create figures contained in the manuscript "Ultraviolet astronomical spectrograph calibration with laser frequency combs from nanophotonic lithium niobatewaveguides"</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>
Novae Contribution to the Galactic Lithium Enhancement
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017PASP..129g4201R">Novae Contribution to the Galactic Lithium Enhancement</a></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>
Observation of Order and Disorder in Solid-Electrolyte Interphases of Lithium-Metal Anodes
<p>This data was collected by Hyeongjun Koh and made publicly available. It was also used as a dataset for an article submission.</p> <p> </p> <p>Please cite this reference when you would like to use it.</p>
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>
Dataset: Stack pressure on lithium-ion pouch cells: a comparative study of constant pressure and fixed displacement devices
<p>Dataset for the paper Experimental Investigation of Power Available in Lithium-Ion Batteries.</p> <ul> <li>Battery: Melasta LCO 6.8Ah pouch cell</li> </ul> <p>Static Pressure Loss:</p> <ul> <li>Test 01: Fixed displacement device</li> <li>Test 02: Constant pressure (springs)</li> <li>Test 03: Constant pressure (pneumatic)</li> </ul> <p>Dynamic pressure variation:</p> <ul> <li>Test 04</li> </ul> <p>Spring stiffness investigation:</p> <ul> <li>Test 05</li> </ul> <p> </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>
Experimental Data for "Substantial Oxygen Loss and Chemical Expansion in Lithium-Rich Layered Oxides at Moderate Delithiation"
<p>This zip file contains all experimental data for the publication "Substantial Oxygen Loss and Chemical Expansion in Lithium-Rich Layered Oxides at Moderate Delithiation ". Within each folder, there are README text files which explain how to interpret the files contained therein. </p>
DFT datasets for training machine-learning potential to model Cl-doped lithium borosilicate glasses using DeePMD
<h2><strong>Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using</strong></h2> <h2><strong>a machine-learning simulation</strong></h2> <h5>Shingo Urata, Noriyoshi Kayaba</h5> <ul> <li>DFT_Data_for_Cl-doped_LBSCl_glass.zip inlucudes atom configurations, energies, forces, box size, atom types, atom kinds, and virial in coord.raw, energy.raw, force.raw, type.raw, type_map.raw, and virial.raw, respectively. </li> <li>All DFT data were evaluated using PBE with a cutoff energy of 600 eV by VASP.</li> <li>The other detasets are available from https://doi.org/10.5281/zenodo.10577559</li> <li>LBSCl_DMD_model.pb is force field developed using DeePMD-kit.</li> <li>LBSCl_DMD_model_c.pb.zip is the compressed version of LBSCl_DMD_model.pb.</li> </ul>
Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: a case study on low temperature charging
<p>Dataset and code of the equilibrium model. They refer to publication <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.fub.2024.100006" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.fub.2024.100006</span></span></a></p>
Unveiling the Autocatalytic Growth of Li2S Crystals at the Solid-Liquid Interface in Lithium-Sulfur Batteries
<p>Electrocatalysts have been extensively employed to suppress the shuttling effect in lithium-sulfur (Li-S) batteries. However, it remains challenging to probe the sulfur redox reactions and mechanism at the electrocatalyst/LiPS interface after the active sites are covered by the solid discharge products Li2S/Li2S2. Here, we demonstrate the intrinsic autocatalytic activity of the Li2S (100) plane towards lithium polysulfides on single-atom nickel (SANi) electrocatalysts. Guided by theoretical models and experimental data, it is concluded that LiPS dissociates into Li2S2 and short-chain LiPS on the Li2S (100) plane. Subsequently, Li2S2 undergoes further lithiation to Li2S on the Li2S (100) surface, generating a new Li2S (100) layer, thus enabling the autocatalytic formation of a new Li2S (100) surface. Benefiting from the autocatalytic growth of Li2S, the concentration of LiPS in the electrolyte remains at a lower level, enabling Li-S batteries under high loading and low electrolyte conditions to exhibit superior electrochemical performance.</p>
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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.