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441 results for “Battery”
THE BATTERY MATERIALS SOURCING ENGINEER - Mark Huijben - University of Twente
<p>The need for radically different usage of our planet's resources has never been so high. There is an increasing demand for electricity in the years to come. We need innovative ways to store that energy to take it out whenever and wherever we need it. </p> <p>Professor in Nanomaterials for Energy Conversion and Storage, Mark Huijben elaborates on the development of next-level batteries. Not only having optimal performance but also being more sustainable. That begins with the design of a battery. Also, the materials being used make a huge difference in their recyclability. </p> <p>Gerwin Hoogsteen, a researcher on energy management for smart grids, surprises us with a creative perspective on energy usage and storage. He takes us to the year 2030 where energy is stored locally, in self-driving cars that drive to places with energy overload and take it to the place where you need it: your home. What hurdles do we need to take to make this a reality?</p>
Trajectories used for Tailoring Charge Transfer Kinetics in Organic Radical Batteries
<p>Trajectories, which are discussed in our work "Tailored Charge Transfer Kinetics in Organic Radical Batteries - A Joint Synthetic-Theoretical Approach"</p> <p>The trajectories were obtained by linearly interpolating in internal coordinates (LIICs) of the relaxed ground state species of molecules A to F, where the charge is either localized on the thiophene backbone (B) or the TEMPO moiety (T1 and T2). Thereby, the program suite pysisyphus (Steinmetzer <em>et. al.</em> 2021) was used to obtain the LIICs. Endpoints represent the fully optimized redox species. The trajectories were used to calculate the intramolecular charge transfer reactions.</p> <p>Trajectories of the following charge transfer reactions are uploaded:</p> <p>1. A<sub>B</sub>→A<sub>T1</sub> (ABtAT1.trj)</p> <p>2. A<sub>B</sub>→A<sub>T2 </sub>(ABtAT2.trj)</p> <p>3. B<sub>B</sub>→B<sub>T1 </sub>(BBtBT1.trj)</p> <p>4. B<sub>B</sub>→B<sub>T2 </sub>(BBtBT2.trj)</p> <p>5. C<sub>B</sub>→C<sub>T1 </sub>(CBtCT1.trj)</p> <p>6. C<sub>B</sub>→C<sub>T2 </sub>(CBtCT2.trj)</p> <p>7. D<sub>B</sub>→D<sub>T1 </sub>(DBtDT1.trj)</p> <p>8. D<sub>B</sub>→D<sub>T2 </sub>(DBtDT2.trj)</p> <p>9. E<sub>B</sub>→E<sub>T </sub>(EBtET.trj)</p> <p>10. F<sub>B</sub>→F<sub>T </sub>(FBtFT.trj)</p>
Neural network for Lithium Metal Battery
<p>Electric cars are an integral part of our clean energy future – every time one replaces a gas-powered vehicle, it can save 1.5 tons of carbon dioxide per year. But to truly expand the population and reach of electric cars, new energy storage solutions must be developed to produce lighter vehicles with longer ranges and more powerful batteries.</p> <p>A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.</p> <p>The research team, led by Berkeley Lab’s Daniela Ushizima, a staff scientist in the Applied Mathematics and Computational Research Division and a Berkeley Institute for Data Science Research Affiliate, includes scientists from the National Fuel Cell Research Center (NFRC) at UC Irvine and collaborators from UC Berkeley’s Department of Electrical Engineering and Computer Sciences and School of Information. Together, they created these deep-learning algorithms to automate the inspection of batteries with data acquired using advanced instruments, including those at Berkeley Lab’s Advanced Light Source (ALS). By using X-ray tomography as the input data, as well as prototypes defined by battery experts, the research team developed automated methods to detect battery defects in rechargeable lithium metal batteries and measure their growth during battery cycling.</p> <p>The researchers focused on solid-state lithium metal batteries (LMB), which are different from traditional lithium-ion batteries in that they use solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. Some of the challenges of this new technology are predicting battery cycling stability and preventing the formation of lithium dendrite growth, Ushizima noted. This harmful phenomenon may occur during LMB charge and discharge, when lithium can deposit irregularly, building up dendrites (lithium plating) that lead to failures, such as short-circuiting. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography (XRT) scans. Machine learning algorithms and multiscale representation of XRT from LMB samples enable the quantification of LMB defects.</p>
Dataset: American Battery Technology Company (ABAT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Electra Battery Materials Corporation (ELBM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Multisensor measurement of healthy adult performance during standardised motor function test battery
<p>This dataset contains inertial data from 4 wearable sensor nodes and 1 wearable patch worn by 20 healthy adult participants performing a series of physical functioning tests (including the short physical performance battery, the timed up and go test, a walking test and balance tests). Details of patient demographics, the physical functioning tests and of each sensor are contained in files in the main folder.</p> <p>Inertial data (accelerometer and gyroscope) is contained in two folders relating to each sensor type. The start and end time for each sensor can be taken from the details in each folder structure, as detailed below. The times given are specific to each sensor's monitoring system which are not exactly synchronised. As such, a manual synchronisation shaking protocol was followed where all sensors were strapped together and shaken three times in succession at the start of each data collection period. The physical functioning test times will also need to be synchronised.</p> <p>-> Inertial sensor data / (subject id).zip / (subject id) / (date_time_crossTest_SD_session#) /<br> -> Wearable inertial patch / (subject id) / (date)T(time) /</p>
Inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs"
<p>This file contains the main inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs".</p>
Energy measurements for analyzing medical-implant-battery lifetime
<p>The dataset comprises of energy-consumption measurements pertaining to a modern ultra-low-power MCU (EFM32TG11). These numbers were used to evaluate the impact of using certain cryptographic primitives on the battery lifetime of implantable medical devices.</p> <p>Further details can be found in the relevant publication:</p> <p>Muhammad Ali Siddiqi and Christos Strydis. 2019. IMD security vs. energy: are we tilting at windmills?: POSTER. In <em>Proceedings of the 16th ACM International Conference on Computing Frontiers</em> (CF '19). ACM, New York, NY, USA, 283-285. DOI: https://doi.org/10.1145/3310273.3323421</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>
Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset
<p>In the retired batteries sustainable utilization scenario, preprocessing steps such as capacity grading and consistency matching is essential to determine how a battery should be reused or recycled. The conventional approach of measuring battery state of health (SOH), charge-discharge profiles, and other related properties through long-time charge-discharge cycle is both time-consuming and energy-intensive. Therefore, developing rapid, non-invasive, and sustainable preprocessing methods for randomly retired batteries is crucial. However, actual measured data in this field are very limited, both in terms of the quantity of retired batteries and the diversity of battery chemistries and materials. To address this gap, we open-source this Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset to foster further academic research and industrial applications in the field of battery SOH fast estimation and consistency fast assessment. Xiamen Lijing New Energy Technology Co., Ltd., collected this dataset. The collaboration team at Tsinghua Berkeley Shenzhen Institute (TBSI) processed this dataset and utilized generative models for data augmentation, significantly enhancing the economic feasibility of large-scale battery repurposing.</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 for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Dataset for publication: "Magnesium and Aluminum in Contact with Liquid Battery Electrolytes: Ion Transport through Interphases and in the Bulk"
<div> </div> <div> <p>This is the experimental raw data set associated with the following publication: M. Löw, J. Grill, MM May, and J. Popovic-Neuber, Magnesium and Aluminium in Contact with Liquid Battery Electrolyte: Ion Transport through Interphases and in the Bulk, ACS Material Letters (2024). DOI:10.1021/acsmaterialslett.4c01589</p> <p>The data set is organized according to the publication's figures. </p> </div>
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour
<p>This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.</p>
Data set: The Effect of Chlorides on the Performance of DME / Mg[B(hfip)4]2 Solutions for Rechargeable Mg Batteries
<p>Dataset of the continuum simulations generated and used within the paper "<span>The Effect of Chlorides on the Performance of DME / Mg[B(hfip)</span><span>4</span><span>]</span><span>2 </span><span>Solutions for Rechargeable Mg Batteries</span>", published in <span>Journal of The Electrochemical Society</span> (<span>2023</span><span>, </span><span>170 (9)</span><span>, 090542</span>, DOI: <span>10.1149/1945-7111/acf960</span>).</p> <p><span>One of the major issues in developing electrolyte solutions for rechargeable magnesium batteries is understanding the positive effect of chloride anions on Mg deposition-dissolution processes on the anode side, as well as intercalation-deintercalation of Mg</span><span>2+ </span><span>ions on the cathode side. Our previous results suggested that Cl</span><span>– </span><span>ions are adsorbed on the surface of Mg anodes and Chevrel phase Mg</span><span>x</span><span>Mo</span><span>6</span><span>S</span><span>8 </span><span>cathodes. This creates a surface add-layer that reduces the activation energy for the interfacial Mg ions transportation and related charge transfer, as well as promotes the transport of Mg</span><span>2+ </span><span>from the solution phase to the Mg anode surface and into the cathodes’ host materials. Here, this work further examines the effect of adding chlorides to the state-of-the-art Mg[B(hfip)</span><span>4</span><span>]</span><span>2 </span><span>/ DME electrolyte solution, specifically focusing on reversible magnesium deposition, as well as the performance of Mg cells with benchmark Chevrel phase cathodes. It was observed that the presence of chlorides in these solutions facilitates both Mg deposition, and Mg</span><span>2+ </span><span>ions intercalation, whereby this effect is more pronounced as the purity levels of the solution is lowered.</span> </p>
Data set: Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "<span>Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance</span>", published in ChemSusChem (<span>2021</span><span>, </span><span>14 (21)</span><span>, 4820-4835, DOI: <span>10.1002/cssc.202101498</span></span>).</p> <p><span>The performance of rechargeable magnesium batteries is strongly dependent on the choice of electrolyte. The desolvation of multivalent cations usually goes along with high energy barriers, which can have a crucial impact on the plating reaction. This can lead to significantly higher overpotentials for magnesium deposition compared to magnesium dissolution. In this work we combine experimental measurements with DFT calculations and continuum modeling to analyze magnesium deposition in various solvents. Jointly, these methods provide a better understanding of the electrode reactions and especially the magnesium deposition mechanism. Thereby, a kinetic model for electrochemical reactions at metal electrodes is developed, which explicitly couples desolvation to electron transfer and, furthermore, qualitatively takes into account effects of the electrochemical double layer. The influence of different solvents on the battery performance is studied for<br>the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate electrolyte salt. It becomes apparent that not necessarily a whole solvent molecule must be stripped from the</span> <span>solvated magnesium cation before the first reduction step can take place. For magnesium reduction it seems to be sufficient to have one coordination site available, so that the magnesium cation is able to get closer to the electrode surface. Thereby, the initial desolvation of the magnesium cation determines the deposition reaction for mono-, tri- and tetraglyme, whereas the influence of the desolvation on the plating reaction is minor for diglyme and<br>tetrahydrofuran. Overall, we can give a clear recommendation for diglyme to be applied as solvent in magnesium electrolytes</span>.<br><br></p>
Data set: Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance", published in ChemSusChem (<span>2020</span><span>, </span><span>13 (14)</span><span>, 3599-3604, </span>DOI: 10.1002/cssc.202001034).</p> <p><br>The choice of electrolyte has a crucial influence on the performance of rechargeable magnesium batteries. In multivalent electrolytes an agglomeration of ions to pairs or bigger clusters may affect the transport in the<br>electrolyte and the reaction at the electrodes. In this work the formation of clusters is included in a general model for magnesium batteries. In this model, the effect of cluster formation on transport, thermodynamics and kinetics is consistently taken into account. The model is used to analyze the effect of ion clustering in magnesium tetrakis(hexafluoroisopropyloxy)borate in dimethoxyethane as electrolyte. It becomes apparent that ion agglomeration is able to explain experimentally observed phenomena at high salt concentrations. </p>
In situ polyaniline coating of Prussian blue as cathode material for sodium-ion battery
<p>Prussian blue has great potential for using as a sodium cathode material owing to its high working potential and cube frame structure. Herein, this work reports a two-step method to synthesize Prussian blue with ascorbic acid (AA) as the ball-milling additive, which improves electrochemical rate performance of Prussian blue during the traditional co-precipitation method. The obtained Prussian blue sample exhibited a superior specific capability (113.3 mAh g<sup>-1</sup> even at 20 C, 1 C=170 mA g<sup>-1</sup>) and a specific capacity retention of 84.8% after 100 cycles at 1 C rate. In order to enhance the cycling performance of the Prussian blue, an in situ polyaniline (PANI) coating strategy was employed in which aniline was added into the electrolyte and polymerized under electrochemical conditions. The coated anode exhibited a high specific capacity retention of 62.7% after 500 cycles, which is significantly higher than that of the non-coated sample which only remains 40.1% after 500 cycles. This development has shown a great potential as a low-cost, high-performance and environmental-friendly technology for large-scale industrial application of PB.</p>
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>
ScienceDex guides
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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)
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DANDI Archive for NWB datasets
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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.