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73 results for “lithium-ion”
Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"
<p>Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a></p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.</p> <p> </p> <p><strong>Break-in cycles:</strong></p> <p>Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.</p> <p> </p> <p><strong>Ageing Conditions:</strong></p> <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> <p>For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge.</p> <p>For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.</p> <p> </p> <p><strong>Reference Performance Tests (RPTs):</strong></p> <p>All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures.</p> <p>Long RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> <p>Short RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol> <p> </p> <p><strong>Extracted Data - Main </strong></p> <p>One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes:</p> <p>Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>Ageing Cycles: number of ageing cycles the cell has been subject to. *this is <strong>not </strong>equivalent full cycles.</p> <p>Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C.</p> <p>Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah.</p> <p>Energy Throughput: as with "charge throughput", but for energy. Units: Wh.</p> <p>C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh.</p> <p>C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh.</p> <p>0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.</p> <p> </p> <p><strong>Extracted Data - Degradation Modes:</strong></p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge.</p> <p>The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain:</p> <p>Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode.</p> <p>Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.</p> <p> </p> <p><strong>Timeseries data from RPTs:</strong></p> <p>Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.</p> <p> </p> <p><strong>Jupyter Notebook:</strong></p> <p>A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.</p> <p> </p> <p><strong>Notes:</strong></p> <p>A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.</p>
Dataset and Model for "Understanding the limits to short-range order suppression in many-component disordered rock salt lithium-ion cathode materials"
<p>Data and Model For "Understanding the limits to Short-range order Suppression in Many-Component Disordered Rock Salt Lithium-ion Cathode Materials"</p> <p>Paper DOI: 10.1039/D3TA02088F</p> <p>Raw data (VASP calculations) and production model for the associated paper.</p> <p>- `cluster_expansions` contains the final model used for the analysis in the<br> paper. The model can be loaded using the `icet` cluster expansion package<br> using </p> <p> ```<br> from icet import ClusterExpansion # requires the icet python package</p> <p> ce = ClusterExpansion.read("cluster_expansion.ce")<br> ```<br> <br> see the icet docs for how to manipulate this object: https://icet.materialsmodeling.org/</p> <p>- `raw_data` contains the data used to fit the model. The calculation data<br> contains the raw vasp calculations in tar.gz files, and each training<br> generation has an associated `.json` files. The files contain Pymatgen<br> ComputedStructureEntry objects. Probably the easiest way to load these <br> into a python script is </p> <p> ```<br> from monty serialization import loadfn # requires the monty package<br> <br> training_data = loadfn("calculation_data.json")<br> ```</p> <p>- element references contains VASP calculations (stored as above) for <br> the elemental reference calculations used to determine the formation<br> energies of the training structures</p>
Dataset for Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions"</strong> published in Small (<a href="https://doi.org/10.1002/smll.202303442">https://doi.org/10.1002/smll.202303442</a>)</p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Scanning electrochemical cell microscopy (SECCM) data</li> <li>Scanning electron microscopy (SEM) images</li> <li>Secondary ion mass spectrometry (SIMS) data</li> <li>Transmission electron microscopy (TEM) images</li> <li>X-ray photoelectron spectroscopy (XPS) data</li> </ul>
Simulation data of Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468
<p>This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468".</p> <p>Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations.</p> <p>The provided lzip archive is structured into separate folders, one per simulation. Each folder contains the output data and a short README.txt with further hints. For information on the compression algorithm and how to uncompress it lzip please refer to https://en.wikipedia.org/wiki/Lzip.</p>
Circularity of lithium-ion battery materials in electric vehicles
Open the record for dataset details and reuse information.
Experimental data for "Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models"
<p>This dataset is for the validation data in Chen et al. (2020). It contains data for three different LG M50 cells undergoing an experiment in which the cells are charged in a constant-current/constant-voltage fashion and discharge at a constant current for different C-rates (C/10, C/2, 1C and 1.5C). Apart from the current and voltage, the temperatures of the cell surface and the thermal chamber in which they are cycled is recorded too.</p> <p><strong>References:</strong></p> <p>Chang-Hui Chen <em>et al</em> 2020 <em>J. Electrochem. Soc.</em> <strong>167</strong> 080534 (<a href="https://doi.org/10.1149/1945-7111/ab9050">https://doi.org/10.1149/1945-7111/ab9050</a>)</p>
Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"
<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains: </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>· Energy throughput: as with "charge throughput", but for energy.</p> <p>· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p> <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p> {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>
Dataset for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries
<p>A dataset for publication Datase for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries including all relevant data used in the manuscript. Information on how to use the dataset are included in the readme file.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset – version 2: result data]
<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 data from 228 commercial NMC/C+SiO lithium-ion cells aged for almost 600 days under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells.<br>This dataset is an update to the dataset previously published under the DOI 10.35097/1947 and described in the publication with the DOI 10.1038/s41597-024-03831-x. This dataset only includes result data (capacity, impedance, and pulse resistance measurements). The log data is published under the DOI 10.35097/kww7jv8ajuvchcah.</p>
Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset – version 2: log data]
<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 almost 600 days under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells.<br>This dataset is an update to the dataset previously published under the DOI 10.35097/1947 and described in the publication with the DOI 10.1038/s41597-024-03831-x. This dataset only includes log data. The result data is published under the DOI 10.35097/1969.</p>
Dataset for 'Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging'
<p>This is the dataset used in our paper '<em>Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging</em>', which mainly includes data from <strong>40Ah batteries</strong> at <strong>0.3C, 1C, 2C</strong>, and two <strong>280Ah batteries</strong> at <strong>20%, 60%, and 100% DOD</strong>, respectively. For specific details, please refer to Readme.text</p> <p>The data in<strong> LISHEN</strong> is the cycle test data of 40Ah battery at different discharge depths under 100%,60%,20%DOD working condition. The data in <strong>CATL</strong> and<strong> EVE </strong>is the cyclic test data of 280Ah battery at different discharge depths under 100%,60%,20%DOD conditions. <br>The CSV file has 21 or 17 columns, (17 columns for, data sequence number, good cycling, step number, step type, time (s) , total time (s) , current (a) , voltage (V) , capacity (AH) , charging capacity (AH) , discharge capacity (AH) , energy (Wh) , charging energy (Wh) , discharge energy (Wh) , absolute time (s) , power (W) , temperature (° C-RRB-) , the 21-column CSV adds four metrics (DQ/DV (AH/V) in column 17, dQm/DV (mAh/V) in column 18, contact resistance (m ω) in column 19, and module start-stop status in column 20) , More detailed files can be found in the zip file.</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>
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>
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>
3D micro/nano-CT datasets of sandstone rock, lithium-ion battery, and fuel cell used for testing P3T-Net
<p>These are the datasets 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>Dataset contains overall 8 3D images of 4 cases. Each case contains images from the target domain and source domain. </p> <p>Case 1. Target domain: micro-CT image of a 9-hour long scan of a sandstone (voxel size: 2.15um); Source domain: micro-CT image of a 7-minute fast scan of a sandstone (voxel size: 2.15um).</p> <p>Case 2. Target domain: micro-CT image of a 7-hour long scan of a sandstone (voxel size: 3.28um); Source domain: synchrotron micro-CT image of a 24-second fast scan of a sandstone (voxel size: 6.75um).</p> <p>Case 3. Target domain: nano-CT image of a dual-mode scan of Lithium-ion battery cathode (voxel size: 128nm); Source domain: three nano-CT images of a single-mode scan of lithium-ion battery cathode (two absorption and one phase mode) (voxel size: 128nm).</p> <p>Case 4. Target domain: micro-CT image of a 3-hour long scan of a fuel cell (voxel size: 2.25um); Source domain: micro-CT image of a 10-minute fast scan of a fuel cell (voxel size: 2.25um).</p> <p>For any further questions or requirements, please contact kunning.tang@unsw.edu.au.</p>
Research Data for "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials"
<p>This are the datasets supporting the figures and tables in the manuscript and supporting information of the publication "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials".</p> <p>Available in ACS Energy Letters under <span><a href="https://doi.org/10.1021/acsenergylett.4c02106"><span>https://doi.org/10.1021/acsenergylett.4c02106</span></a></span></p>
Dataset of "Thermal Stability of Valuable Metals in Lithium-Ion Battery Cathode Materials: Temperature Range 500-800 °C"
<p>Examination of the cathode part of a lithium-ion battery, which is composed of NMC spinel 622 (LiNi0.6Mn0.2Co0.2O2), and investigation of its stability during annealing at temperatures ranging from 500-800 °C. Determination of valuable metals (Li, Ni, Mn and Co) after exposure to temperatures from 500 to 800 °C using ICP-OES analysis. The correlation between element stability and the temperature of PVDF release, a binder in cathodes, was demonstrated by DTA. SEM-EDS performed local chemical analysis. XRD characterised the crystalline structures of the original material and changes after annealing at 800 °C. This work builds on Part I, which focusses on the thermal stability of the material in lower temperature ranges. This research aimed to gain a deeper understanding of the pyrometallurgical aspect of the recycling process and identify the ideal annealing temperature for maximising the recovery of valuable metals. </p>
Online Repository for "Sorting lithium-ion battery electrode materials using dielectrophoresis"
<p>Please see the readme file.</p> <p>The matlab script for evaluating the measurements is called “Eval_Fluoro.m” and can be found in this repository.</p> <p>The excel sheet “20221028_photometric_iron.xlsx” contaiins the data from the chemical analysis.</p> <p>The manufacturing data for the electrodes is provided in the zip folder: PCB_boards_Giesler.zip and can be uploaded to a manufacturer of choice.</p>
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Allen Brain Atlas
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