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441 results for “Battery”
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 and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries
<p>This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties.</p> <p>The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-qz055">https://doi.org/10.26434/chemrxiv-2022-qz055</a>.</p> <p>All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP).</p> <p>The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>) are stored, is provided in the following table:</p> <table> <tbody> <tr> <td>Convergence_study</td> <td>Figure S1</td> </tr> <tr> <td>Bulk_properties</td> <td>Table S3</td> </tr> <tr> <td>Surface_properties</td> <td>Table 1, Table 2, Figure 1, Table S5</td> </tr> <tr> <td>Adsorption_properties</td> <td>Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9</td> </tr> <tr> <td>Diffusion_properties</td> <td>Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9</td> </tr> </tbody> </table> <p> </p>
Impedance-based forecasting of battery performance amid uneven usage
<p>Dataset of 88 commercial lithium-ion coin cells cycled under multistage constant current charging/discharging, with currents randomly changed between cycles to emulate realistic use patterns.</p> <p>raw-data.zip contains the following data:</p> <p>Variable Discharge: We subject 24 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V.</p> <p>Fixed Discharge: We subject an additional 16 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to the same cycling conditions as above, except now fixing the discharge current for all cells and cycles at 52.5mA (1.5C) instead of randomly changing the discharge current at each cycle.</p> <p>chemistry2-25C.zip contains the following data:</p> <p>Variable Discharge @ 25C: We subject 32 RS-Pro LiR2032 coin cells (of nominal capacity 1C = 40mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. The distributions of currents are varied across different cell batches. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V.</p> <p>Variable Discharge @ 35C: We repeat the experiment conducted above for 16 additional RSPro cells, except that now we cycle the cells at 35C instead of 25C.</p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Dataset for the publication: Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries
<p>This dataset contains all input and output files of the performed calculations, which results were published in the ChemSusChem article: 'Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries' (<a href="https://doi.org/10.1002/cssc.202201821">https://doi.org/10.1002/cssc.202201821</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-3chph">https://doi.org/10.26434/chemrxiv-2022-3chph</a>.</p>
Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries
<p>Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript.</p> <p>Codes and mechanical parts of the project:</p> <p>AutoBASS 2.0: <a href="https://github.com/Helge-Stein-Group/AutoBASS/tree/AutoBASS_2.0">GitHub - Helge-Stein-Group/AutoBASS at AutoBASS_2.0</a></p>
Dataset for publication "Scaling of planar sodium-metal chloride battery cells to 90 cm2 active area"
<p><span lang="EN-US">Sodium-metal chloride batteries; high-temperature ZEBRA batteries; molten-salt batteries; scalable stationary energy storage; alkali-metal anode</span></p>
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>
A Modified Doyle-Fuller-Newman Model Enables the Macroscale Physical Simulation of Dual-ion Batteries - Dataset and Software
<p>This dataset contains:</p> <p>- all the raw cycling data of the three-electrode cell used to gather the experimental data for the model validation (VMP data, exported with EC-LAB);<br>- the specific, processed data used in the model validation step (0.2C discharge, 5C discharge, EIS data);<br>- the COMSOL dual-ion battery model (version 6.0). IMPORTANT: activate the "Electric potential at the positive electrode current collector (only for EIS)" boundary condition when simulating impedance spectroscopy, and deactivate it when simulating charge/discharge curves; the charge-discharge profile can be modified by changing the duration of the test, the C-rate, and the conditions set in the "Events" section.</p> <p>Update: Fixed the model to work also in the 6.2 version of COMSOL (Substituted Dleff with Dleffxx in the modified weak expression of the cathode mass conservation equation). Download the new version!</p>
Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3
<p>The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context".</p> <p>The second file S3 contains the economic data and the climate change impacts of the same article.</p> <p>In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.</p> <p> </p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Dataset for "Sustainable Disposal and End-of-Life Treatment of Battery Energy Storage Systems: An Environmental and Economic Case Study"
This study investigates the environmental and economic impacts of end-of-life (EOL) treatment for a 2.8 MWh/2.5 MW battery energy storage system (BESS) based on lithium-ion batteries (LIBs). It focuses on recycling pre-treatment processes for battery systems and recycling procedures for components like cooling systems, fire extinguishing systems, inverters, and the reuse of BESS containers and substations. A life cycle assessment (LCA) was employed to evaluate key environmental impacts, including climate change, eutrophication, and resource use. The study reveals substantial environmental benefits, particularly from recovering secondary materials like aluminium and copper, with recycling pre-treatment contributing significantly to overall benefits. Additionally, the economic analysis projects profits, emphasizing the advantages of locally sourcing critical raw materials. The research highlights the need for more sustainable recycling practices and provides insights for improving environmental and economic strategies in BESS management, offering guidance for future research and policy development in battery waste processing.
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Dataset for publication "Cell design strategies for sodium-zinc chloride (Na-ZnCl2) batteries, and first demonstration of tubular cells with 38 Ah capacity"
<p><span lang="EN-US">stationary energy storage; ZEBRA battery; high-temperature metal chloride battery; molten-salt battery; molten sodium anode.</span></p> <p>Measured data to recreate Figures 1-8 in the above manuscript.</p>
Research data supporting "Tin phosphide anodes for potassium-ion batteries: insights from crystal structure prediction"
<p>This dataset contains the output files of crystal structure prediction calculations (density-functional theory relaxations, bandstructures, phonon calculations, GIPAW-NMR calculations) on the ternary K-Sn-P phase diagram. All calculations were performed with the CASTEP DFT package (https://www.castep.org/) and the "matador" Python library (https://github.com/ml-evs/matador).</p> <p><strong>Contents:</strong></p> <ul> <li>"convergence_tests.zip": contains the results of convergence tests on the K-P system at two levels of accuracy "polish" and "searches" on the corresponding edge of the K-Sn-P ternary system</li> <li>"phonons.zip": contains CASTEP output files for phonon calculations on the predicted low-lying phases on the corresponding edge of the K-Sn-P phase diagram</li> <li>"polish.zip": contains CASTEP output files of relaxations on the corresponding edge of the K-Sn-P system at the "polish" level of accuracy using various different xc-functionals or external pressures.</li> <li>"searches.zip": contains ".res" files that provide the relaxed structure from each different crystal structure prediction method on the corresponding edge of the K-Sn-P system.</li> <li>"bulk_modulus.zip" contains CASTEP output files for calculation of E(V) curves for low-lying KP phases with different xc-functionals.</li> <li>"nmr.zip" contains CASTEP output files for GIPAW-NMR calculations of chemical shifts for low-lying K-Sn-P phases.</li> <li>"spectral.zip" contains CASTEP and OptaDOS output files for projected bandstructure and DOS calculations of low-lying K-Sn-P phases.</li> <li>"digests.zip" contains JSON representations of all the structures from polish and searches, broken down into K-P and K-Sn-P specific digests.</li> </ul>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X</p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>trav_time</p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:second</p> </td> </tr> <tr> <td> <p>trav_time_min</p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time</p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)</p> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p>
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