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78 results for “Electric Vehicle”
Dataset: "Auralization of Electric Vehicles for the Perceptual Evaluation of Acoustic Vehicle Alerting Systems"
<p>This repository contains audio examples and measurement data accompanying the paper: </p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The Matlab code for the corresponding auralization model can be found at: <a href="https://github.com/leonpaulmueller/evat" target="_blank" rel="noopener">https://github.com/leonpaulmueller/evat</a></p> <p> </p> <p><strong>Content</strong></p> <ul> <li><code>audio_examples.zip</code> <ul> <li>avas - Measured and synthesized AVAS source signals</li> <li>passby - Measured and auralized binaural EV passages at roadside observer position. The generated signals use the same vehicle velocity as the corresponding measurements.</li> <li>tire - Measured and synthesized tire/road noise source signals</li> </ul> </li> <li><code>measurements.zip</code> <ul> <li>ambience - binaural ambience measurements</li> <li>avas - AVAS source signal measurements</li> <li>passby - Binaural pass-by measurements, including velocity data and isolated AVAS and tire/road noise signals</li> <li>tires - tire/road noise measurements</li> </ul> </li> </ul> <p> </p> <p>For consistency with the paper, we use the following aliases for the three evaluated vehicles:</p> <ul> <li>Vehicle A: Tesla Model Y 2021</li> <li>Vehicle B: Volkswagen ID.3 Pro Performance 2021</li> <li>Vehicle C: Nissan Leaf 2018</li> </ul>
A novel liquid cooling plate concept for thermal management of lithium-ion batteries in electric vehicles
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A novel hybrid thermal management approach towards high-voltage battery pack for electric vehicles
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A New Concept of Air Cooling and Heat Pipe for Electric Vehicles in Fast Discharging
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Advanced Hybrid Battery Thermal Management System for Fast Charging of Electric Vehicles
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Figures for Thermal Management of the Li‐Ion Batteries to Improve the Performance of the Electric Vehicles Applications
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Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
How to charge while drive: Scheduling point-to-point deliveries of an electric vehicle under overhead wiring
<p>Test data used in Section 5.2</p> <p> </p> <p>|J|_|R|_L_l_h.txt</p> <p>|J|: number of jobs<br> |R|: number of ramps<br> L: highway length<br> l: share of electrification<br> h: instance</p> <p>|J|;|R|;l;</p> <p>|J|: number of jobs<br> |R|: number of ramps<br> l: share of electrification</p> <p>Truck (C;c0;rd;r0;delta+;delta-;)</p> <p>C: maximum charge level<br> c0: initial charge level<br> rd: final position<br> r0: initial position<br> delta+: charging rate<br> delta-: consumption rate</p> <p>Path (p0=0; p1; ...; p|R|=L;)</p> <p>pr: position of ramp r</p> <p>Electrified (#;first elec?; sect):</p> <p>#: number of sections<br> elec?: true => first section electrified (alternating)<br> false => first section NOT electrified (alternating)<br> sect: list of sections<br> (Note: two directions! 0-L and L-0)</p> <p>Jobs (j;lo/delta-;ld/delta-;rd;ro)</p> <p>j: Index<br> lo/delta-: (Total travel distance beyond the highway for picking up)/consumption rate<br> ld/delta-: (Total travel distance beyond the highway for delivering)/consumption rate<br> rd: Ramp where the ECV leaves the highway to deliver<br> ro: Ramp where the ECV leaves the highway to pick up</p> <p>Jobs (ldo):</p> <p>j: Index<br> j': Index<br> ldo: Total travel distance beyond the highway for delivering job j and picking up j' with rdj = roj'<br> (>C: exceeds capacity)</p>
Quantitative evidence for modelling electric vehicles - Supplementary Data
<p><strong>Please cite as:</strong></p> <p>Malte Jansen, Rob Gross, and Iain Staffell. ‘Quantitative Evidence for Modelling Electric Vehicles’. <em>Renewable and Sustainable Energy Reviews</em> 199 (1 July 2024): 114524. <a href="https://doi.org/10.1016/j.rser.2024.114524">https://doi.org/10.1016/j.rser.2024.114524</a>.</p> <p><strong>Abstract:</strong></p> <p>Electric vehicles are now a major contributor to decarbonising the transport sector. Their rollout has accelerated rapidly since 2020, reaching a global fleet of 40 million in 2023. This presents both problems and opportunities for electricity systems, with charging increasing peak loads, but also providing a large new source of flexibility to help manage increased shares of wind and solar generation, shift peak demand and improve network management.</p> <p>While EV flexibility is widely discussed, there is uncertainty surrounding the magnitude to which EVs could help electricity systems, and a distinct lack of quantitative evidence around adoption, charging behaviour and technical capabilities for load shifting. This study employs the rapid evidence assessment method to synthesise recent information. We find that studies expect that EVs could provide 1–11 GW of flexible capacity per million vehicles (median: 3.7 GW), with the ability to shift demand by 1.5–5 hours (median: 4 hours) and a price elasticity of –0.77 to –0.10 (median: –0.15). Diurnal profiles of charging demand and availability for providing flexibility are aggregated across multiple studies. The results are relevant for energy modellers and show that the interaction between EVs and electricity systems can be generalised on a widely-applicable basis.</p>
Electric Vehicle Fast-Charging Software: Architectural Considerations Towards Trustworthiness
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Dataset for: "On the role of electric vehicles towards low-carbon energy systems: Italy and Germany in comparison"
<p>Input files for EnergyPlan models (Italy and Germany energy systems in 2016).</p> <p>See EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
Open Data for SLR Artificial Intelligence and Electric Vehicles
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Dataset and code for "Higher labor intensity in US automotive assembly plants after transitioning to electric vehicles"
<p>Dataset and code for the article: "<span>Higher labor intensity in US automotive assembly </span><span>plants after transitioning to electric vehicles" submitted to Nature Communications. </span></p>
Consumer Attitudes towards Electric Vehicles in Jabodetabek
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Data for project: Discontinuance among California’s electric vehicle buyers: Why are some consumers abandoning their electric vehicles?
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Market Adoption of Electric Vehicles in Indonesia
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Analysis of Circular Price Prediction Strategy for Used Electric Vehicles
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Supporting data for "Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing"
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Smart Charging of Future Electric Vehicles Using Roadway Infrastructure
<p>Corresponding data set for Tran-SET Project No. 18ITSTSA03. Abstract of the final report is stated below for reference:</p> <p>"Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this study presented the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology and power electronics. The project introduced a novel wireless charging system, SIC (Smart Illuminative Charging), that uses LEDs powered by piezoelectric nanomaterials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle’s battery. Through the project, the energy-harvestable 2D nanomaterials (EH2Ns) were tested for their mechanical-to-electrical energy conversion capabilities and the relatively large-area EH2N samples (2cm x 2cm) produced high output voltages of up to 52mV upon mechanical pressure. An electrically conductive glass fiber reinforced polymer (GFRP) was developed to be used as physical support in the integrated SIC system. Furthermore, a lab-scale prototype device was developed to testify the mechanism of illuminative charging. The project team was able to prove the feasibility of SIC concept and the start to end conversion efficiency was calculated to be 40%. The project team also provided field implementation recommended framework based on the results from the small-scale prototype developed. The framework discussed how the developed SIC can be implemented in the field and what are the expected outcomes. The team recommended inserting the EH2N embedded in the GFRP, the LEDs and the needed circuitry in the wheel path of the vehicles on the pavement by cutting a sawtooth compartment with a width of 18’’ and a length of 8’ every couple of miles. On the vehicle, a PV array will be placed on the underside between the wheel wells of each side of the EV to capture the illumination from the LEDs embedded in the roadway. The detailed strategy is presented in this report."</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
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)
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.