Lithium rose to 94,400 CNY/T on December 2, 2025, up 0.05% from the previous day. Over the past month, Lithium's price has risen 16.54%, and is up 20.64% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lithium - values, historical data, forecasts and news - updated on December of 2025.

Dataset Overview

This dataset provides a comprehensive view of the aging process of lithium-ion batteries, facilitating the estimation of their Remaining Useful Life (RUL). Originally sourced from NASA's open repository, the dataset has undergone meticulous preprocessing to enhance its analytical utility. The data is presented in a user-friendly CSV format after extracting relevant features from the original .mat files.

Key Features of the Dataset

1. Battery Performance Metrics:

   • Capacity: Measured over time to assess degradation.
   • Internal Resistance (Re): Represents the electrical resistance of the battery.
   • Charge Transfer Resistance (Rct): Indicates charge movement efficiency.

2. Environmental Conditions:

   • Ambient Temperature: External temperature affecting battery performance.

3. Identification Attributes:

   • Battery ID: Unique identifier for each battery tested.
   • Test ID: Links specific test conditions to outcomes.
   • UID & Filename: Traceable dataset references.

4. Processed Data:

   • Missing values have been addressed.
   • Columns irrelevant to RUL estimation have been removed.
   • Skewness in the data has been corrected for statistical accuracy.

5. Labels:

   • Degradation States: Categorized into intervals for easier interpretation.
   • Ranges include operational and failure states.

Potential Applications

1. Battery Health Monitoring:

   • Predict battery failure timelines.
   • Enhance battery maintenance strategies.

2. Data Science and Machine Learning:

   • Model development for RUL prediction.
   • Feature engineering for predictive analysis.

3. Research and Development:

   • Improve battery design.
   • Study the impact of environmental and operational conditions on battery life.

Technical Details

• File Format: CSV
• Size: ~625.02 kB
• Columns: 9
• Data Points: Multiple observations across various tests.

Tags

• Keywords: Lithium-ion batteries, RUL, Battery Aging, Machine Learning, Data Analysis, Predictive Maintenance.

License

• Apache 2.0: Permits academic and commercial use.

Usage Instructions

1. Import the dataset into your data analysis tools (e.g., Python, R, MATLAB).
2. Explore features to understand correlations and dependencies.
3. Use machine learning models for RUL prediction.

Provenance

The dataset was retrieved from NASA's publicly available data repositories. It has been preprocessed to align with research and industrial standards for usability in analytical tasks.

Call to Action

Leverage this dataset to enhance your understanding of lithium-ion battery degradation and build models that could revolutionize energy storage solutions.

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.

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.
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.
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.

Lithium is crucial in lithium-ion batteries (LIBs), serving as a main component of the electrolyte and cathode. Elements such as cobalt, nickel, and manganese are also vital for high performance, energy density, and stability. This study aimed to examine the behaviour of end- of-life cathode material (LiNi0.6Mn0.2Co0.2O2) and its valuable metals after exposure to temperatures between 100 and 400 °C, comparing it with untreated material. The lithium content cannot be reliably determined by conventional analytical methods, so inductively coupled plasma optical emission spectroscopy (ICP-OES) was chosen for this purpose. For ICP-OES measurements, samples were dissolved in different solvents for a specified time, and the concentrations of lithium, nickel, manganese, and cobalt were measured. From the measured values, their theoretical yields were calculated. Due to the annealing at given temperatures and subsequent dissolution, this step can be considered as the first stage of the pyrometallurgical- hydrometallurgical process used in battery recycling. The study was complemented by further analyses to monitor the effect of annealing temperatures on the properties of the material. Based on the results, it was found that the highest theoretical yield in this temperature range was for material annealed at 400 °C and dissolved in 20% nitric acid for 4 hours.

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.

All of the datasets and the below description are quoted from Project - Data-driven prediction of battery cycle life before capacity degradation.

Objective

This dataset, used in our publication “Data-driven prediction of battery cycle life before capacity degradation”, consists of 124 commercial lithium-ion batteries cycled to failure under fast-charging conditions. These lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were cycled in horizontal cylindrical fixtures on a 48-channel Arbin LBT potentiostat in a forced convection temperature chamber set to 30°C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.

The objective of this work is to optimize fast charging for lithium-ion batteries. As such, all cells in this dataset are charged with a one-step or two-step fast-charging policy. This policy has the format “C1(Q1)-C2”, in which C1 and C2 are the first and second constant-current steps, respectively, and Q1 is the state-of-charge (SOC, %) at which the currents switch. The second current step ends at 80% SOC, after which the cells charge at 1C CC-CV. The upper and lower cutoff potentials are 3.6 V and 2.0 V, respectively, which are consistent with the manufacturer’s specifications. These cutoff potentials are fixed for all current steps, including fast charging; after some cycling, the cells may hit the upper cutoff potential during fast charging, leading to significant constant-voltage charging. All cells discharge at 4C.

The dataset is divided into three “batches”, representing approximately 48 cells each. Each batch is defined by a “batch date”, or the date the tests were started. Each batch has a few irregularities, as detailed on the page for each batch.

The data is provided in two formats. For each batch, a MATLAB struct is available. The struct provides a convenient data container in which the data for each cycle is easily accessible. This struct can be loaded in either MATLAB or python (via the h5py package). Pandas dataframes can be generated via the provided code. Additionally, the raw data for each cell is available as a CSV file. Note that the CSV files occasionally exhibit errors in both test time and step time in which the test time resets to zero mid-cycle; these errors are corrected for in the structs.

The temperature measurements are performed by attaching a Type T thermocouple with thermal epoxy (OMEGATHERM 201) and Kapton tape to the exposed cell can after stripping a small section of the plastic insulation. Note that the temperature measurements are not perfectly reliable; the thermal contact between the thermocouple and the cell can may vary substantially, and the thermocouple sometimes loses contact during cycling.

Internal resistance measurements were obtained during charging at 80% SOC by averaging 10 pulses of ±3.6C with a pulse width of 30 ms (2017-05-12 and 2017-06-30) or 33 ms (2018-04-12).

The following repository contains some starter code to load the datasets in either MATLAB or python:

https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Low rate data used to generate figure 4:

• 2018-02-20_batchdata_updated_struct_errorcorrect.mat
• 2018-04-03_varcharge_batchdata_updated_struct_errorcorrect.mat

If using this dataset in a publication, please cite: Severson et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy volume 4, pages 383–391 (2019).

**Batch - 2017-05-12**
Experimental design
- All cells were cycled with one-step or two-step charging policies. The charging time varies from ~8 to 13.3 minutes (0-80% SOC). There are generally two cells tested per policy, with the exception of 3.6C(80%).
- 1 minute and 1 second rests were placed after reaching 80% SOC during charging and after discharging, respectively.
-We cycle to 80% of nominal capacity (0.88 Ah).
- An initial C/10 cycle was performed in the beginning of each test.
- The cutoff currents for the constant-voltage steps were C/50 for both charge and discharge.
- The pulse width of the IR test is 30 ms.

Experimental notes
- The computer automatically restarted twice. As such, there are some time gaps in the data.
- The temperature control is somewhat inconsistent, leading to variability in the baseline chamber temperature.
- The tests in channels 4 and 8 did not successfully start and thus do not have data.
- The thermocouples for channels 15 and 16 were accidentally switched.

Data notes
- Cycle 1 data is not available in the struct. The sampling rate for this cycle was initially too high, so we excluded it from the data set to create more manageable file sizes.
- The cells in Channels 1, 2, 3, 5, and 6 (3.6C(80%) and 4C(80%) policies) were stopped at the end of this batch and resume...

Intro

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

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.

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.

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.

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.

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.

Data

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’.

Summary Data

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.

It contains:

Performance Summary

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.

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 ACS publication for more details.

Data includes:

· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.

· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.

· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.

· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.

· 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).

· 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).

· Energy throughput: as with "charge throughput", but for energy.

· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.

· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.

· 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).

· 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.

· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.

· 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.

· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.

Ageing Sets Summary

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.

Processed Timeseries data

Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.

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.

Raw Data

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.

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

This dataset provides detailed specifications for Lithium-Ion Batteries including cost, lifespan, weight, and charging time.

Total-Other-Finance-Cost Time Series for Shenzhen Dynanonic Co Ltd. Shenzhen Dynanonic Co., Ltd engages in the research and development, production, and sale of materials for lithium-ion batteries in China. It offers nano lithium iron phosphate, lithium iron phosphate, and lithium supplement enhancers for use in new energy vehicles, energy storage, and other industries. The company was founded in 2007 and is headquartered in Shenzhen, China.

A set of Li-ion batteries were run through different operational profiles (charge, discharge and impedance) at various temperatures. Impedance measurement was carried out through an electrochemical impedance spectroscopy (EIS) frequency.

Repeated charge and discharge cycles result in accelerated aging of the batteries while impedance measurements provide insight into the internal battery parameters that change as aging progresses. The experiments were stopped when the batteries reached end-of-life (EOL) criteria. These datasets can be used for the prediction of both remaining charge (for a given discharge cycle) and remaining useful life (RUL). Data are in Batch of 6 experiments, data provided in .mat files with experiment details in associated READEME.txt -

Fires involving lithium batteries are the fastest growing fire risk in London. This dataset provides a breakdown of all Electrical Vehicle and lithium-ion related fires from Calendar Year 2017 onwards: The first tab provides data on incidents The second tab provides figures on fatalities and injuries. The data is updated monthly, one month in arrears. The London Fire Commissioner is the fire and rescue authority for London and runs the London Fire Brigade. For more information on how to store lithium battery produces, click here.

About this dataset

Context

The dataset tabulates the population of Lithium by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lithium. The dataset can be utilized to understand the population distribution of Lithium by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lithium. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lithium.

Key observations

Largest age group (population): Male # 15-19 years (13) | Female # 45-49 years (14). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Lithium population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lithium is shown in the following column.
• Population (Female): The female population in the Lithium is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lithium for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lithium Population by Gender. You can refer the same here

Battery Degradation Dataset - LGM50 Calendar Aging Study

This repository contains an experimental dataset used in the research article: “Lithium-Ion Battery Degradation Modelling Using Universal Differential Equations: Development of a Cost-Effective Parameterisation Methodology, Kuzhiyil et al., Applied Energy, 2025, https://doi.org/10.1016/j.apenergy.2024.125221”. Please cite this article if you are using this dataset.

Dataset Overview

The dataset presents comprehensive calendar aging data collected from commercial LGM50 lithium-ion cells under controlled storage conditions:

• Storage Temperatures: 0°C, 25°C, and 45°C
• State of Charge (SOC): 13 distinct levels per temperature condition
• Test Duration: Two-years per condition (on average).

The dataset consists of MATLAB (.mat) files containing cell cycling results from Reference Performance Tests (RPTs) conducted throughout the aging study.

This data release provides the descriptions of approximately 20 U.S. sites that include mineral regions, mines, and mineral occurrences (deposits and prospects) that contain enrichments of lithium (Li). This release includes sites that have a contained resource and (or) past production of lithium metal greater than 15,000 metric tons. Sites in this database occur in Arkansas, California, Nevada, North Carolina, and Utah. There are several deposits that were not included in the database because they did not meet the cutoff requirement, and those occur in Arizona, Colorado, the New England area, New Mexico, South Dakota, and Wyoming. In the United States, lithium was first mined from pegmatite orebodies in South Dakota in the late 1800s. The Kings Mountain pegmatite belt of North Carolina also had significant production from pegmatites, and the area may still contain as much as 750 million metric tons (Mt) of ore containing 5 Mt lithium metal (Kesler and others, 2012). In 2018, U.S. production of lithium was restricted to a single lithium-brine mining operation in Nevada. In 2018, the U.S. had a net import reliance as a percentage of apparent consumption of more than 50 percent for lithium (U.S. Geological Survey, 2019). The U.S. is not a significant producer of lithium, so the commodity is mainly imported from Chile and Argentina to meet consumer demand. Lithium is necessary for strategic, consumer, and commercial applications. The primary uses for lithium are in batteries, ceramics, glass, metallurgy, pharmaceuticals, and polymers (U.S. Geological Survey, 2019). Lithium has excellent electrical conductivity and low density (lithium metal will float on water), making it an ideal component for battery manufacturing. Lithium is traded in three primary forms: mineral concentrates, mineral compounds (from brines), and refined metal (electrolysis from lithium chloride). Lithium mineralogy is diverse; it occurs in a variety of pegmatite minerals such as spodumene, lepidolite, amblygonite, and in the clay mineral hectorite. Current global production of lithium is dominated by pegmatite and closed-basin brine deposits, but there are significant resources in lithium-bearing clay minerals, oilfield brines, and geothermal brines (Bradley and others, 2017). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as National Instrument 43-101 (NI 43-101) or the Joint Ore Reserves Committee Code (JORC Code). The inclusion of a particular lithium mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the larger lithium deposits in the United States, which are diverse in their geology and resource potential. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Bradley, D.C., Stillings, L.L., Jaskula, B.W., Munk, LeeAnn, and McCauley, A.D., 2017, Lithium, chap. K of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. K1–K21, https://doi.org/10.3133/pp1802K. Kesler, S.E., Gruber, P.W., Medina, P.A., Keoleian, G.A., Everson, M.P., and Wallington, T.J., 2012, Global lithium resources—relative importance of pegmatite, brine and other deposits: Ore Geology Reviews, v. 48, October ed., p. 55—69. U.S. Geological Survey, 2019, Mineral commodity summaries 2019: U.S. Geological Survey, 200 p., https://doi.org/10.3133/70202434.

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. 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. 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.

Log / measurement data (raw and processed time series data) related to this result dataset: - https://www.kaggle.com/datasets/matthiasluh/battery-aging-dataset-measurement-data-v2/

Dataset description: - https://www.nature.com/articles/s41597-024-03831-x - https://publikationen.bibliothek.kit.edu/1000174456 (Chapter 7)

Old version of the dataset: - Impedance data: https://www.kaggle.com/datasets/matthiasluh/battery-aging-dataset-impedance-v1-1/ - Everything else: https://www.kaggle.com/datasets/matthiasluh/battery-aging-dataset-capacity-and-impedance-v01/

The ongoing energy crisis has made it imperative to develop low-cost, easily fabricated, yet efficient materials. It is highly desirable for these nanomaterials to function effectively in multiple applications. Among transition metal dichalcogenides, tungsten diselenide (WSe2) shows great promise but remains understudied. In this work, we doped WSe2 with Mn using a simple hydrothermal method. The resulting material exhibited excellent electrocatalytic activity for the hydrogen evolution reaction, achieving a low overpotential of –0.28 V vs RHE at -10 mA/cm2, enhanced conductivity, and high stability and durability. Moreover, as an anode material in in lithium-ion batteries, the Mn-doped WSe2 outperformed pristine WSe2, reaching discharge and charge capacities of 1223 and 922 mAh g−1, respectively. Additionally, the Mn-doped material maintained a significantly higher discharge capacity of 201 mAh g−1 compared to intact WSe2, which had 68 mAh g−1 after 150 cycles. This work offers novel insights into designing efficient bifunctional nanomaterials using transition metal dichalcogenides.

This record constitutes the raw data underlying the paper "Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients" (preprint link) The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:

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.

The lithium-sulfur (Li-S) battery has received a lot of attention because it is characterized by high theoretical energy density (2,600 Wh/kg) and low cost. Though many works on the “shuttle effect” of polysulfide have been investigated, lithium metal anode is a more challenging problem, which leads to a short life, low coulombic efficiency, and safety issues related to dendrites. As a result, the amelioration of lithium metal anode is an important means to improve the performance of lithium sulfur battery. In this paper, improvement methods on lithium metal anode for lithium sulfur batteries, including adding electrolyte additives, using solid, and/or gel polymer electrolyte, modifying separators, applying a protective coating, and providing host materials for lithium deposition, are mainly reviewed. In addition, some challenging problems, and further promising directions are also pointed out for future research and development of lithium metal for Li-S batteries.

This dataset is a cleaned subset of the following dataset:

Paper: LiPo batteries dataset: Capacity, electrochemical impedance spectra, and fit of equivalent circuit model at various states-of-charge and states-of-health Dataset Link: LiPo Battery LP-503562-IS-3 EIS, Capacity, ECM Data

From the original paper:

This dataset contains experimental data of capacity and electrochemical impedance of five Lithium Polymer (LiPo) batteries (model LP-503562-IS-3 manufactured by BAK Technology). All batteries have been subjected to hundreds of charge-discharge cycles to obtain their characteristics at different states-of-health. Capacities have been measured under both standard and stress conditions.

Specifications of the batteries tested:

• Nominal Capacity: 1.1 Ah
• Nominal Voltage: 3.7 V (standard for LiPo).
  • Equivalently, 1S -- 1 cell in Series
  • https://www.rogershobbycenter.com/lipoguide
• LiPo cells are safe down to a voltage of 3.0V per cell.
  • More conservative ratings have 3.2V or 3.3V as lowest safe threshold

There are three files in this dataset:

• cycle_voltage_extracted_charge_std.csv: Compares the voltage of the battery to the extracted charge in amp-hours Ah (i.e. how much charge we have pulled out of the battery). The cycle number of the battery is included as well (i.e. how many times the battery has been charged and discharged). This file represents "standard" conditions for charging and discharging.
• cycle_voltage_extracted_charge_stress.csv: Same columns as previous file, but data was taken under "stress-testing" conditions for the batteries. This file has a size of 63M, which is still fairly small but may take a bit to plot.
• cycle_vs_capacity.csv: Compares the cycle number to total battery capacity in Ah.

This data is shared under a CC BY 4.0 license.