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LLMs for EV Infrastructure Permitting
The Electric Vehicle (EV) charging permitting processes' database is a novel, multi-jurisdictional resource designed to contain the required codes and compliances in a structured database. Within this database are three tables, each structured with 287 columns, designed to capture detailed information spanning electrical, structural, zoning, and accessibility aspects, along with data regarding fees, reviews, and process durations. The database contains 99 state-level documents pertaining to 36 U.S. states, in addition to 87 county-level and 101 city-level documents, thus offering a complete overview of guidance and practices regarding permitting.
The data was gathered via an Azure-hosted GPT-4o workflow, supplemented by targeted manual Google searches. State and county materials were located and extracted using the GPT-4o model. The Large Language Models (LLM) were used in conjunction with the decision tree framework with targeted prompts to extract the key information. The structured database incorporates Tables 1-3 included below as resources, as well as Table 4 which provides the scores for each document based on the scoring criteria in the paper (to be added after publication). The database can be used to compare and identify the patterns and trends in the requirements across different authorities having jurisdictions. This resource can be used by researchers, policymakers, and project teams.
Note: LLMs are known to make mistakes in interpreting complex procedural documents and therefore no one should rely solely on this database to inform their own real-world EV infrastructure projects.
Other content to navigate the database can be found in the "Database Information" resource below.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Umapriya Renganathan",
"@type": "vcard:Contact",
"hasEmail": "mailto:umapriya.renganathan@nrel.gov"
}
|
| dataQuality |
true
|
| description | The Electric Vehicle (EV) charging permitting processes' database is a novel, multi-jurisdictional resource designed to contain the required codes and compliances in a structured database. Within this database are three tables, each structured with 287 columns, designed to capture detailed information spanning electrical, structural, zoning, and accessibility aspects, along with data regarding fees, reviews, and process durations. The database contains 99 state-level documents pertaining to 36 U.S. states, in addition to 87 county-level and 101 city-level documents, thus offering a complete overview of guidance and practices regarding permitting. The data was gathered via an Azure-hosted GPT-4o workflow, supplemented by targeted manual Google searches. State and county materials were located and extracted using the GPT-4o model. The Large Language Models (LLM) were used in conjunction with the decision tree framework with targeted prompts to extract the key information. The structured database incorporates Tables 1-3 included below as resources, as well as Table 4 which provides the scores for each document based on the scoring criteria in the paper (to be added after publication). The database can be used to compare and identify the patterns and trends in the requirements across different authorities having jurisdictions. This resource can be used by researchers, policymakers, and project teams. Note: LLMs are known to make mistakes in interpreting complex procedural documents and therefore no one should rely solely on this database to inform their own real-world EV infrastructure projects. Other content to navigate the database can be found in the "Database Information" resource below. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "EV-ELM Repository",
"format": "HTML",
"accessURL": "https://github.com/NREL/EV-ELM",
"mediaType": "text/html",
"description": "The Electric Vehicle Permitting Processes with the Energy Language Model (EV-ELM) project builds upon prior research that employed Large Language Models (LLMs) to locate, retrieve, and interpret policy data concerning energy infrastructure. This application leverages LLMs to identify documents concerning permitting and installation procedures for electric vehicle charging infrastructure. The software retrieves, and analyzes these documents, storing the output information in a structured database.
"
},
{
"@type": "dcat:Distribution",
"title": "1. Database Flowchart.png",
"format": "png",
"mediaType": "image/png",
"description": "A flowchart showing the details populated in every data table.",
"downloadURL": "https://data.openei.org/files/8540/Database_Flowchart.png"
},
{
"@type": "dcat:Distribution",
"title": "Table 4 - Simplified Permit Scores.csv",
"format": "csv",
"mediaType": "text/csv",
"description": "A table of documents in the database scored based on the scoring criteria in the paper.",
"downloadURL": "https://data.openei.org/files/8540/Table4_simplified_permit_scores.csv"
},
{
"@type": "dcat:Distribution",
"title": "0. Database Information.docx",
"format": "docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "An information abstract to help users navigate the database flowchart resource.
",
"downloadURL": "https://data.openei.org/files/8540/LLM_database_information%20%281%29.docx"
},
{
"@type": "dcat:Distribution",
"title": "Filename Mapping.csv",
"format": "csv",
"mediaType": "text/csv",
"description": "Contains the structure to locate the original file as downloaded with the respective Jurisdiction-level, Filename and "document_id".",
"downloadURL": "https://data.openei.org/files/8540/filename_mapping.csv"
},
{
"@type": "dcat:Distribution",
"title": "State Documents.zip",
"format": "zip",
"mediaType": "application/zip",
"description": "Documents for state-level EV charging permits, in PDF or TXT format, are gathered for parsing.",
"downloadURL": "https://data.openei.org/files/8540/State.zip"
},
{
"@type": "dcat:Distribution",
"title": "Table 1 - Permit Overall.xlsx",
"format": "xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "Table 1 provides a summary of permit requirements and procedures for both commercial and residential projects, detailing the application process, associated costs, review procedures, and estimated timeframes.",
"downloadURL": "https://data.openei.org/files/8540/Table1_Permit_overall_WVXUA.xlsx"
},
{
"@type": "dcat:Distribution",
"title": "Table 2 - Permit Electrical.xlsx",
"format": "xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "Table 2 focuses on electrical codes, standards, and technical specifications specifically for EV charging stations.",
"downloadURL": "https://data.openei.org/files/8540/Table2_Permit_elec_NmXIY.xlsx"
},
{
"@type": "dcat:Distribution",
"title": "Table 3 - Permit Other.xlsx",
"format": "xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "Table 3 combines accessibility, building structure, and zoning regulations that dictate site design and adherence.",
"downloadURL": "https://data.openei.org/files/8540/Table3_Permit_other_E363x.xlsx"
},
{
"@type": "dcat:Distribution",
"title": "Parsed Documents.zip",
"format": "zip",
"mediaType": "application/zip",
"description": "The "parsed documents" are the text files, cleaned and parsed, containing raw EV charging permit information extracted from city, county, and state PDFs. The tables are created and queried using these files.",
"downloadURL": "https://data.openei.org/files/8540/parsed_doc.zip"
},
{
"@type": "dcat:Distribution",
"title": "City Documents.zip",
"format": "zip",
"mediaType": "application/zip",
"description": "The city-level EV charging permit documents collected as pdf or txt files and used for parsing.",
"downloadURL": "https://data.openei.org/files/8540/City.zip"
},
{
"@type": "dcat:Distribution",
"title": "County Documents.zip",
"format": "zip",
"mediaType": "application/zip",
"description": "The county-level EV charging permit documents collected as pdf or txt files and used for parsing",
"downloadURL": "https://data.openei.org/files/8540/County.zip"
}
]
|
| identifier | https://data.openei.org/submissions/8540 |
| issued | 2025-09-24T06:00:00Z |
| keyword |
[
"Accessibility requirements",
"Building requirements",
"EV",
"EV charging",
"Electrical requirements",
"LLM",
"Permitting process",
"Transportation",
"United States",
"Zoning",
"accessibility",
"code",
"compliance",
"data",
"database",
"electric vehicle",
"electrical",
"energy",
"large language model",
"model",
"permit",
"permitting",
"raw data",
"structural"
]
|
| landingPage | https://data.openei.org/submissions/8540 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2026-01-12T17:45:02Z |
| programCode |
[
"019:003"
]
|
| projectNumber |
"52838"
|
| projectTitle | LLMs for EV Infrastructure Permitting |
| publisher |
{
"name": "National Renewable Energy Lab (NREL)",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-125.4514,24.5873],[-66.5318,24.5873],[-66.5318,49.2637],[-125.4514,49.2637],[-125.4514,24.5873]]]}"
|
| title | LLMs for EV Infrastructure Permitting |
