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78 results for “Electric Vehicles”
Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles - summary results
<p>Summary results for submitted article: Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles</p> <p> </p>
DATABASE: Electric Vehicle, Battery and Smart Grid patent citation networks and main paths.
<p>This dataset comprises the original patent citation networks that were created to calculate the main citation paths for the technologies of Electric Vehicle, Battery and Smart Grid.</p> <p>For each technology (1- Electric Vehicle, 2- Battery, 3- Smart Grid), four outputs are provided:</p> <p>a- Patent extraction: USPTO patents filtered by IPC or CPC and found in the Triadic Patent Families database (OECD, 2021) </p> <p>b- Full nodes and links reconstructed by following patent citations through a snowball method (until no further patents found)</p> <p>c- Filtered nodes and links according to keywords</p> <p>d- Main path nodes and links (with citation weights).</p> <p>For a detailed explanation of the methodology please refer to the submitted paper:</p> <p><strong>Transitions as a coevolutionary process: the urban emergence of electric vehicle inventions</strong></p>
Circularity of lithium-ion battery materials in electric vehicles
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dataset figures - Assessing the Performance of Fuel Cell Electric Vehicles Using Synthetic Hydrogen Fuel - Article Energies
<p>Dataset for Table 1 - 2 and for Figure 4</p>
Dataset: "Auditory Localization of Multiple Stationary Electric Vehicles"
<p>This repository contains data accompanying the publication "Auditory Localization of Multiple Stationary Electric Vehicles", published in the <em>Journal of the Acoustical Society of America (JASA)</em>)</p> <p>The following abbreviations are used in the filenames and data</p> <p> </p> <table> <tbody> <tr> <td>c</td> <td>Combustion Noise</td> </tr> <tr> <td>n</td> <td>Noise AVAS</td> </tr> <tr> <td>t</td> <td>Two-Tone AVAS</td> </tr> <tr> <td>m</td> <td>Multi-Tone AVAS</td> </tr> <tr> <td>le</td> <td>Localization Error in °</td> </tr> <tr> <td>lt</td> <td>Localization Time in s</td> </tr> <tr> <td>fl</td> <td>Failed Localizations in %</td> </tr> </tbody> </table> <p> </p> <p>The dataset contains:</p> <ul> <li><em>stimuli.zip</em>: Sound pressure of all evaluated stimuli as calibrated 32-bit float .wav files</li> <li><em>trials.zip</em>: Binaural sound pressure recordings of all 72 trials as calibrated 32-bit float .wav files, including parking lot background noise. These recordings were obtained by placing a HeadAcoustics HMS-V artificial head at the listening position. The files are named as <em>TrialNr_Stim1Abbreviation_Stim1PositionInDegree_Stim2Abbreviation_Stim2PositionInDegree_Stim3Abbreviation_Stim3PositionInDegree.wav</em></li> <li><em>experimentProcedure.mp4</em>: Video showcasing the experiment procedure.</li> <li><em>rawData.xlsx</em>: The unprocessed experiment data. Each row represents one individual trial.</li> <li><em>processedData.xlsx</em>: The pre-processed experiment data for each participant. Each column represents the mean localization error (le), mean localization time (lt), or percentage of failed detections (fd) for a single stimulus. E.g., <em>le_mn_m</em> is the mean localization error of the Multi-Tone AVAS, averaged for all 4 trials where Multi-Tone AVAS and Noise AVAS were played simultaneously (TrialNr. 61-64, see Table in Paper). <em>fl_ccc</em> is the percentage of failed localizations for the Combustion Noise in the 4 trials where three combustion sounds were played simultaneously. For the statistical evaluations, participants "HZPS", "TGCI" and "XKOU" have been removed as outliers.</li> </ul> <p> </p> <p>All stimuli are compliant with both UNECE R138 and US FMVSS 141 as illustrated in Figure 3 of the paper.</p>
Instances and solutions for the multi-depot electric vehicle scheduling problem with the objective of minimizing the fleet size (EVSP-MD-FS)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models”.</p>
Electrification of Transportation Means a Lot More Than a Lot More Electric Vehicles
<p>Energy use in 2050 from the Annual Energy Outlook 2021 published by the U.S. Department of Energy's Energy Information Agency.</p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
Vehicle-to-grid Response on 13 February 2024 in Australian National Electricity Market
<p>This data captures the response of 16 Nissan LEAF electric vehicles to a frequency contingency in the Australian National Electricity Market on the 13th of February 2024, which led to widespread blackouts in Melbourne. The data comes from the bidirectional Wallbox Quasar chargers, as well as six high speed power meters located at the grid connection of each of the properties in which the vehicles were charging.</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Energy consumption of 15 electric vehicles (one day resolution)
<p><strong>Energy consumption of 15 electric vehicles (one day resolution)</strong></p> <p>Sérgio Ramos, João Soares, Zahra Foroozandeh, Inês Tavares, Zita Vale</p> <p><strong>Paper title: TODO</strong></p> <p>Type: EV consumption</p> <p>Duration: One year</p> <p>Resolution: One day</p> <p>Application: Paper submitted on</p> <p>Sheets description:</p> <ul> <li>EV 1-15: Contains the information of the energy consumption and initial State of Charge of each EV (kWh).</li> </ul>
Scaling Behavior for Electric Vehicle Chargers and Roadmap to Addressing the Infrastructure Gap
<p>Source code and datasets for "Scaling Behavior for Electric Vehicle Chargers and Roadmap to Addressing the Infrastructure Gap"</p>
Electric vehicle occupancy of charging points in the city of Paris
<p>Dataset collected by EDF R&D using the <em>Paris Data</em> open data platform, providing real-time occupancy of public charging points for electric vehicles in the city of Paris. This <strong>data.zip</strong> archive should be used at the root of the following gitlab repository (it replaces the empty data folder): <a href="https://gitlab.com/smarter-mobility-data-challenge/additional_materials">smarter-mobility-data-challenge/additional_materials</a>. V1 corresponds to the data provided for the Smarter Mobility Challenge, augmented with exogenous features such as weather and traffic. V2 corresponds to additional raw observations of occupancy data collected and accompanied by initial data processing.</p>
A complete energy community dataset with photovoltaic generation, battery energy storage systems and electric vehicles (v1.5)
<p>This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.</p> <p> </p> <p>Version 1.5 update: <span>on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.</span></p> <p> </p> <p>This work has been published in Elsevier's Data in Brief journal:<br><em> Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale<br> Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles<br> Data in Brief, 2023, 109218, ISSN 2352-3409<br> <a href="https://doi.org/10.1016/j.dib.2023.109218.">https://doi.org/10.1016/j.dib.2023.109218</a><br> (<a href="https://www.sciencedirect.com/science/article/pii/S2352340923003372)">https://www.sciencedirect.com/science/article/pii/S2352340923003372)</a></em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.</p> <p> </p> <p>Reference data used to create this dataset:</p> <ul> <li>Filtered energy profiles and renewable energy production profiles: <a href="../record/6778401">https://zenodo.org/record/6778401</a></li> </ul> <ul> <li>Battery storage systems and electric vehicles: <a href="../record/4737293">https://zenodo.org/record/4737293</a></li> </ul>
Understanding the impact of public charging infrastructure on the consideration to purchase an electric vehicle in California
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Developing a vehicle cost calculator to promote electrical vehicle adoption among transportation network company (TNC) drivers
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Destination labels for battery electric vehicles in eVMT dataset
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Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction
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Data from: US-Mexico second-hand electric vehicle trade: Battery circularity and end-of-life policy implications
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Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
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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.