Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “battery aging”
Ageing Data of Lithium-ion "Panasonic NCR18650B" Battery Cells
<p>A large-scale battery aging experiment dataset is presented in this dataset.<br> In total, 116 Lithium-ion cells were cycled in a controlled temperature ambient until they reached the end-of-life.<br> The cycling includes 37 different aging load profiles and more detailed reference tests throughout the whole lifespan of the cells.<br> The load profiles vary in seven parameters (i.e., temperature, charge current, average discharge current, peak discharge current, frequency, average state of charge, and state of charge swing) and were set up by a design of experiment.<br> Different approaches for the estimation of state-of-charge and state-of-health can be provided and analyzed by the offered data set.<br> Thus this dataset forms a broad basis for battery modeling or battery management algorithms for electric vehicles and hybrid electric vehicles.</p> <p>For further details, see the linked publication.</p>
Underlying dataset for battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients
<p>This record constitutes the raw data underlying the paper "<i>Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients</i>" (<a href="https://www.researchsquare.com/article/rs-2535223/v1">preprint link</a>)</p><p>The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:</p><blockquote><p>Practical lithium-ion battery systems require parallelisation of tens to hundreds of cells, however understanding of how pack-level thermal gradients influence lifetime performance remains a research gap. Here we present an experimental study of surface cooled parallel-string battery packs (temperature range 20-45 °C), and identify two main operational modes; convergent degradation with homogeneous temperatures, and (the more detrimental) divergent degradation driven by thermal gradients. We attribute the divergent case to the, often overlooked, cathode impedance growth. This was negatively correlated with temperature and can cause positive feedback where the impedance of cells in parallel diverge over time; increasing heterogeneous current and state-of-charge distributions. These conclusions are supported by current distribution measurements, decoupled impedance measurements and degradation mode analysis. From this, mechanistic explanations are proposed, alongside a publicly available aging dataset, which highlights the critical role of capturing cathode degradation in parallel-connected batteries; a key insight for battery pack developers.</p></blockquote>
Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""
<p>Related to the publication: <a href="https://doi.org/10.1016/j.jpowsour.2021.230459">https://doi.org/10.1016/j.jpowsour.2021.230459</a></p> <p>Datasets for the following Figures:</p> <p>Figure 2.</p> <p>Figure 3.</p> <p>Figure 5.</p> <p>Figure 7.</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>
Understanding the Ageing Processes of Electrolytes in Aqueous Magnesium Batteries Using Radiation Chemistry
<p>Data corresponding to the publication : </p> <p><span>10.1002/batt.202400209</span></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>
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>
Supporting Data for "Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"
<p>This dataset provides the raw data to the manuscript</p> <p><strong>"Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"</strong></p> <p>published in Advanced Energy Materials, <strong>2024</strong>, DOI 10.1002/aenm.202403811</p> <p><a href="https://doi.org/10.1002/aenm.202403811">Link to Publisher</a></p> <p> </p> <p>Comments:</p> <ul> <li>HAXPES synchrotron data is provided as IGOR file (IGOR Pro (v6.37, WaveMaterics Inc.). The file contains the original 2D data and the converted 1D datasets used for analysis</li> <li>In-House XPS data is provided with corresponding peak fits as excel spreadsheet</li> <li>peak data (binding energy (after referencing), intensity, area, FWHM and at.%) is provided in separate excel spreadsheet.</li> </ul> <p> </p>
Addendum to "Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset]"
<p>This is an addendum to the previously published dataset with the title "Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset]" (DOI: 10.35097/1947).<br>In the previous publication, the "cell_eisv2.zip" file was incomplete and only contained data for cells P001_1 to P044_2.<br>The fixed "cell_eisv2_fixed.zip" file of this addendum contains data for all 228 cells P001_1 to P076_3.</p>
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>
Development of a Battery of Audiological Tests for the Precision Diagnosis of Age-related Hearing Loss
ClinicalTrials.gov study NCT05312983. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Li-ion Battery Aging Datasets
This data set has been collected from a custom built battery prognostics testbed at the NASA Ames Prognostics Center of Excellence (PCoE). Li-ion batteries were run through 3 different operational profiles (charge, discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects. Repeated charge and discharge cycles result in accelerated aging of the batteries. The experiments were stopped when the batteries reached the end-of-life (EOL) criteria of 30% fade in rated capacity (from 2 Ah to 1.4 Ah). **Data Acquisition:** The testbed comprises: * Commercially available Li-ion 18650 sized rechargeable batteries, * Programmable 4-channel DC electronic load, * Programmable 4-channel DC power supply, * Voltmeter, ammeter and thermocouple sensor suite, * Custom EIS equipment, * Environmental chamber to impose various operational conditions, * PXI chassis based DAQ and experiment control, and MATLAB based experiment control, data acquisition and prognostics algorithm evaluation setup (appx. data acquisition rate is 10Hz). **Parameter Description:** Data Structure: cycle: top level structure array containing the charge, discharge and impedance operations type: operation type, can be charge, discharge or impedance ambient_temperature: ambient temperature (degree C) time: the date and time of the start of the cycle, in MATLAB date vector format data: data structure containing the measurements for charge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at charger (Amps) Voltage_charge: Voltage measured at charger (Volts) Time: Time vector for the cycle (secs) for discharge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at load (Amps) Voltage_charge: Voltage measured at load (Volts) Time: Time vector for the cycle (secs) Capacity: Battery capacity (Ahr) for discharge till 2.7V for impedance the fields are: Sense_current: Current in sense branch (Amps) Battery_current: Current in battery branch (Amps) Current_ratio: Ratio of the above currents Battery_impedance: Battery impedance (Ohms) computed from raw data Rectified_impedance: Calibrated and smoothed battery impedance (Ohms) Re: Estimated electrolyte resistance (Ohms) Rct: Estimated charge transfer resistance (Ohms) **Intended Use:** The data sets can serve for a variety of purposes. Because these are essentially a large number of Run-to-Failure time series, the data can be set for development of prognostic algorithms. In particular, due to the differences in depth-of-discharge (DOD), the duration of rest periods and intrinsic variability, no two cells have the same state-of-life (SOL) at the same cycle index. The aim is to be able to manage this uncertainty, which is representative of actual usage, and make reliable predictions of Remaining Useful Life (RUL) in both the End-of-Discharge (EOD) and End-of-Life (EOL) contexts.
ScienceDex guides
Understand access before you commit
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.