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73 results for “lithium-ion”
Dataset of "Thermal Stability of Valuable Metals in Lithium-Ion Battery Cathode Materials: Temperature Range 100-400 °C"
<p>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.</p>
Dataset for Gate-to-Gate Life Cycle Assessment of Lithium-Ion Battery Recycling Pre-Treatment
<p>Recycling spent lithium-ion batteries (LIBs) is crucial for improving environmental sustainability and conserving resources. Due to the diversity of LIB applications and recycling technologies, the environmental and energy impacts are not well understood. Comprehensive assessments must consider the distinct operations, methodologies, technology efficiency, and final treatment of materials. This study provides a partial gate-to-gate life cycle analysis (LCA) of a small-scale recycling plant in the Czech Republic, focusing on pre-treatment of spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). The study highlights the benefits of recycling pre-treatment for CECs, significantly reducing environmental impact categories (EICs) such as climate change, eutrophication, and resource use. A high secondary use rate of obtained materials is crucial for environmental benefits, with metal reuse from packaging, connectors, and current collectors being especially important.</p>
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Dataset of "Mn-doped WSe2 as an efficient electrocatalyst for hydrogen production and as anode material for lithium-ion batteries"
<p>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.</p>
Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes
<p>This entry contains the data related to the publication<br><strong>A. Szczęsna-Chrzan <em>et al.</em>, “Ionic conductivity, viscosity, and self-diffusion coefficients of novel imidazole salts for lithium-ion battery electrolytes,”<em> J. Mater. Chem. A</em>, vol. 11, no. 25, pp. 13483–13492, 2023, doi: 10.1039/D3TA01217D.</strong><br><br>It contains experimentally determined conductivity, viscosity and self-diffusion coefficients of anions of the Hückel-type salts lithium 4,5-dicyano-2-(trifluoromethyl)imidazolide (LiTDI), lithium 4,5-dicyano-2-(pentafluoroethyl)imidazolide (LiPDI) and lithium 4,5-dicyano-2-(n‑heptafluoropropyl)imidazolide (LiHDI) for various concentrations of the conducting salts (0 M - 1.5 M) in a solvent mixture containing ethylene carbonate (EC) and ethyl methyl carbonate (EMC) in a ratio of 3:7 by weight.</p> <p>The Python scripts used for the analysis of the NMR data are also included in the dataset.</p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
Data-driven model enhancement of late-life lithium-ion batteries
<p>Suplement dataset for the paper "Data-driven model enhancement of late-life lithium-ion batteries"</p>
Dataset of "Comprehensive Machine Learning Approaches for Modelling the State of Charge of Lithium-ion Batteries"
<p>This paper evaluates three ML approaches for SOC modeling in LIBs: the multilayer perceptron (MLP), long short-term memory (LSTM), and the nonlinear autoregressive with exogenous input (NARX) neural network architectures. These models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy (EIS) data, voltage, and capacity readings for commercial LIB cells. Results indicate that MLP and LSTM are more adaptable with a smaller training dataset (14 samples), while the NARX model required more than 34 out of 67 samples to achieve reasonable accuracy. Additionally, the NARX model is more sensitive to changes in the learning rate (α) and exhibits larger output error deviations. The MLP and LSTM models consistently performed well across various hidden layer sizes, showing no upper bound constraints, whereas the NARX model’s performance deteriorated with certain hidden layer configurations.</p>
Lithium-Ion Batteries in Automated Guided Vehicles (AGVs) dataset for article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"
<p>Dataset of aggregated information related to discharge-only cycles of lithium-ion battery packs employed in Automated Guided Vehicle systems.</p> <p>The dataset supports the study in conference article "Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations"</p>
Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling
<p>Self-discharge data related to the manuscript entitled: 'Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling', submitted to Energy Reports on 26 April 2023.</p>
Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the exothermal data for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a></p> <p> </p>
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>
Porous Organic Polymers with Heterocyclic Crown Ethers for Selective Lithium-Ion Capture
<p>Abstract of the publication: Lithium is a key resource of the 21st century. Despite that, Li is traditionally mined rather than obtained from maritime brines or secondary sources such as spent energy-storage devices owing to the difficulties in Li recovery. Herein, we present a porous organic polymer capable of capturing Li ions from aqueous solutions through highly pre-organized heterocyclic crown ether-like pores in the polymer backbone. These features enable Li+ uptake capacities over 120 mg g-1 and selectivity versus highly competitive ions such as Na+, Ca2+, and Mg2+.</p> <p> </p>
Data for Partial Volume Deflagration Experiments with Synthesized Lithium-Ion Battery Thermal Runaway Effluent Gas.
<p>Refer to Readme.pdf or Readme.md for information on this dataset.<br><br>v1.1 - cleanup<br>v1.2 - readme updates</p>
Dataset and code: Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges
<p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges. Joule (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p>
Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation
<div> <div> <div>This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.</div> <br> <div>All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.</div> <br> <div>Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.</div> </div> </div> <div> </div> <div> <div><strong>Associated Python Library</strong></div> <div>BattGP <a href="https://github.com/JoachimSchaeffer/BattGP" target="_blank" rel="noopener">https://github.com/JoachimSchaeffer/BattGP</a></div> <div>This library contains classes and functions to analyze the data set with Gaussian processes.</div> <div>Furthermore, data visualization functions are part of the library.</div> <div><br><strong>Associated Article<br></strong>Gaussian Process-based Online Health Monitoring and Fault Analysis of Lithium-Ion Battery Systems from Field Data</div> <div>Cell Report Physical Science <br> <div><a href="https://doi.org/10.1016/j.xcrp.2024.102258">https://doi.org/10.1016/j.xcrp.2024.102258</a></div> <div> </div> <div> </div> </div> </div>
Data Repository - Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies
<p>Datasets from "Thermal-electrochemical parametrisation of a lithium-ion battery: mapping Li concentration and temperature dependencies" - Journal of Electrochemical Society.</p> <p>This repository contains parameter values for the electrode solid-state diffusivity, entropic term, exchange current density, electronic conductivity, specific heat capacity, and thermal conductivity.</p>
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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.