EVALUATE: Electric Vehicle Assessment and Leveraging of Unified Models Toward Abatements of Emissions, Phase II [supporting dataset]
-
2024-12-10
Details:
-
Creators:
-
Corporate Creators:
-
Corporate Contributors:
-
Subject/TRT Terms:
-
Publication/ Report Number:
-
DOI:
-
Resource Type:
-
Right Statement:
-
Geographical Coverage:
-
Corporate Publisher:
-
Abstract:The NCST EVALUATE (Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions) project (Phases I and II) develops a rigorous methodology involving a high-fidelity system of systems model (i.e., vehicle powertrain, EV charging profiles and grid dispatch datasets) for the purpose of forecasting the emissions outputs of a class of vehicles and use cases. The Phase I findings explored light duty vehicles (LDV) typical urban commuters and households that operate LDVs for daily personal use. Phase II, presented here, focuses on a series of targeted case studies that extend prior work from LDVs operated by individuals to service-oriented vehicles operated by small and medium businesses. Vehicles used in the present study as representative public service fleets include the following pickup trucks, vans, Medium Duty (MD) delivery vehicles, and refuse trucks. In one of the study’s simulations for a MD use case where a specific marginal grid generating resource is selected on an hourly basis to meet a particular EV charging event, estimated CO2 emissions could be as much as 42% lower than a conventional gasoline vehicle, or as much as 24% higher than a conventional gasoline vehicle. This large variance is purely a function of when and how quickly the vehicle is recharged. This study reveals key insights as follows. (1) higher temporal resolution is important to develop more accurate estimates of EV CO2 emissions. Along with this, EV charge management is imperative for all use cases, and has profound implications on infrastructure and emissions; (2) Hybrid Electric Vehicles (HEVs) often performed as well as EVs in contemporary simulations on the basis of emissions benefits, suggesting that consideration of an array of vehicle technologies is important; (3) there is a growing need to focus on higher rate EV charging applications (e.g., DCFC), and related implications on energy storage, as proxied by large vehicle batteries; (4) The trend toward increasing electrification of the transportation sector will continue in conjunction with electrification across other sectors (e.g., buildings, data centers, industry). As such associated cross-sector planning and study of concomitant emissions must be considered in context of other grid trends. Primary contributions of this effort are the development of new methodologies, integration of sub-system models and independent data sources, and decision support tools that estimate the environmental impacts of vehicle electrification. The study’s methodologies and use cases can enhance understanding and scale up in additional EV-grid applications, sectors and regions.
The total size of the dataset is 2.0 MB. The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. The .csv file is often used for moving tabular data between two different computer programs, due to its open format. The most common software used to open .csv files are Microsoft Excel and RecordEditor, (for more information on .csv files and software, please visit https://www.file-extensions.org/csv-file-extension). The .xlsx and .xls file types are Microsoft Excel files, which can be opened with Excel, and other free available spreadsheet software, such as OpenRefine. The .mat file extension is associated with Ox. Ox is an object-oriented statistical system. At its core is a powerful matrix language, which is complemented by a comprehensive statistical library (for more information on the .mat file type and associated software, please visit https://www.file-extensions.org/mat-file-extension-ox-object-oriented-matrix-programming-language-matrix). A .slx file type is a MATlab file that model of algorithms and physical systems using block diagrams. MATlab is a free program (for more information on .slx files and software, please visit https://www.file-extensions.org/slx-file-extension-matlab-model). A .slxc file is a Simulink cache file. Simulink is a simulation and modeling software by Mathworks (for more information on .slxc files and software, please visit https://www.file-extensions.org/slxc-file-extension). File extension .m is associated with the Objective-C, a general-purpose, object-oriented programming language based on Smalltalk language developed by Apple, Inc (for more information on the .m file type and associated software, please visit https://www.file-extensions.org/m-file-extension).
-
Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2024-01-21. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
Public Access Note: This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation:
Simmons, R. (2024). EVALUATE: Electric Vehicle Assessment and Leveraging of Unified models toward Abatement of Emissions, Phase II [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14347459
-
Format:
-
Funding:
-
Collection(s):
-
Main Document Checksum:
-
Download URL:
-
File Type: