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83 results for “Lithium ions”
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>
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>
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>
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>
DFT data for "The Role of Ion Solvation in Lithium Mediated Nitrogen Reduction"
<p>Density Functional Theory data used in the paper "The Role of Ion Solvation in Lithium Mediated Nitrogen Reduction".</p>
Exothermal data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the exothermal behavior of the cells, which is recorded only when the cell is behaving exothermally in the ARC, above a threshold of 0.02 °C / min.</p> <p>Hence, in the files, time in minutes, temperature on the surface at the center of the cell in °C and the registered temperature rate in °C / min is provided. Cathode chemistry, as well as SOC, is indicated in the file name, each set of parameters has been tested at least twice with another cell, which is indicated with M and consecutive numbering of the test number.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the maximum temperatures for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7867730">https://doi.org/10.5281/zenodo.7867730</a></p> <p> </p> <p> </p>
Regionalized life cycle assessment of present and future lithium production for Li-ion batteries
<p>This dataset contains supplementary data for the following publication: </p> <p>Vanessa Schenker, Christopher Oberschelp, Stephan Pfister,<br> Regionalized life cycle assessment of present and future lithium production for Li-ion batteries,<br> Resources, Conservation and Recycling,<br> Volume 187,<br> 2022,<br> 106611,<br> ISSN 0921-3449,<br> https://doi.org/10.1016/j.resconrec.2022.106611.<br> (https://www.sciencedirect.com/science/article/pii/S0921344922004451)</p>
Assessing Sustainability Potential of Spent Lithium-ion Battery Mining using Deep Eutectic Solvents
<p>This dataset provides technical advantages/disadvantages, economic inputs/outputs, and life cycle environmental impacts of solvometallurgy (deep eutectic solvents), pyrometallurgy, and hydrometallurgy systems for critical metals recycling from spent lithium-ion battery.</p>
Data from: Electrochemical performance of ZnO-coated Li4Ti5O12 composite electrodes for lithium-ion batteries with the voltage ranging from 3 to 0.01 V
Oxide is widely used in modifying cathode and anode materials for lithium ion batteries. In this work, a facial method of radio magnetron sputtering is introduced to deposit a thin film on Li4Ti5O12 composite electrodes. The pristine and modified Li4Ti5O12 electrodes are characterized at an extended voltage range of 3-0.01 V. The reversible capacity reach a high level of 286 mAh g-1, which is a little less than its theoretical capacity (293 mAh g-1). Electrodes modified by ZnO thin films with various thickness show elevated rate capability and improved cycle performance.
Static electric equivalent circuit of commercial lithium-ion battery cells using genetic algorithms
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Dataset for publication Synthesis of tubular MXenes with carbon fiber template and use as anodes in lithium-ion batteries
<p>The dataset contains all relevant data and figures regarding the manuscript "Synthesis of tubular MXenes with carbon fiber template and use as anodes in lithium-ion batteries".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
One-Step Grown Carbonaceous Germanium Nanowires and Their Application as Highly Efficient Lithium-Ion Battery Anodes
<p>Developing a simple, cheap, and scalable synthetic method for the fabrication of functional nanomaterials is crucial. Carbon-based nanowire nanocomposites could play a key role in integrating group IV semiconducting nanomaterials as anodes into Li-ion batteries. Here, we report a very simple, one-pot solvothermal-like growth of carbonaceous germanium (C-Ge) nanowires in a supercritical solvent. C-Ge nanowires are grown just by heating (380–490 °C) a commercially sourced Ge precursor, diphenylgermane (DPG), in supercritical toluene, without any external catalysts or surfactants. The self-seeded nanowires are highly crystalline and very thin, with an average diameter between 11 and 19 nm. The amorphous carbonaceous layer coating on Ge nanowires is formed from the polymerization and condensation of light carbon compounds generated from the decomposition of DPG during the growth process. These carbonaceous Ge nanowires demonstrate impressive electrochemical performance as an anode material for Li-ion batteries with high specific charge values (>1200 mAh g<sup>–1</sup> after 500 cycles), greater than most of the previously reported for other “binder-free” Ge nanowire anode materials, and exceptionally stable capacity retention. The high specific charge values and impressively stable capacity are due to the unique morphology and composition of the nanowires.</p>
EU's Recycled Content Targets of Lithium-ion Batteries Are Likey to Compromise Critical Metal Circularity
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Lithium-ion battery end-of-life life cycle assessment
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Data from: Electrochemical performance of ZnO-coated Li4Ti5O12 composite electrodes for lithium-ion batteries with the voltage ranging from 3 to 0.01 V
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Nanostructured tin phosphite SnHPO3: A high-capacity anode material for lithium-ion batteries
<p>Figures and data of the article "Nanostructured tin phosphite SnHPO<sub>3</sub>: A high-capacity anode material for lithium-ion batteries" by Wissal Tout<sup>1,2</sup>, Mickael Mateos<sup>1</sup>, Junxian Zhang<sup>1</sup>, M’hamed Oubla<sup>2</sup>, Nicolas Emery<sup>1</sup>, Eric Leroy<sup>1</sup>, Pierre Dubot<sup>1</sup>, Fouzia Cherkaoui El Moursli<sup>2</sup>, Zineb Edfouf<sup>2</sup>, Fermin Cuevas<sup>1</sup></p> <p><em>(1) </em><em>Univ Paris-Est Créteil, CNRS, ICMPE (UMR 7182), 2 rue Henri Dunant, F-94320 Thiais, France</em></p> <p><em>(2) </em><em>MANAPSE, Faculty of Sciences, Mohammed V<sup>th</sup>, University in Rabat, Morocco</em></p> <p> </p> <p><strong>Images</strong> (Figures 2 and 5) are in tif format (<strong>*.tif</strong>). Open with any visualisation image software</p> <p><strong>Graphs </strong>(Figures 1, 2h, 3, 4, 6, 7, 8, 9) in Origin software program (<strong>*.opj</strong>). Their <strong>corresponding data are provided in ASCII files (*.dat) </strong> that can be opened with any text-reading software</p> <p> </p> <p> </p> <p> </p>
Brain Ion Homeostasis, Lithium and Bipolar Disorder
ClinicalTrials.gov study NCT02727127. IPD Sharing: NO. Countries: 1. Publications: 0.
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.*
Optimal sizing and lifetime investigation of second life lithium-ion battery for grid-scale stationary application
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