Automated Controller Hardware-In-The-Loop Testbed for EV Charger Resilience Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper focuses on the development of a tool that includes an automated testbed with controls, protection, and communications integrated into a real-time system to provide a platform to generate data sets for failure modes and effects analysis. This tool establishes a value for automation of data generation for different scenarios and addresses the gap of nonexistent field data for different applications and use cases. The features of this tool can further be expanded to include multiple power electronics models, communication protocols, and scaled system architectures. This general framework was evaluated for a DC fast charger system use case to provide quantitative solution for resiliency.

Original languageEnglish
Title of host publication2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350397420
DOIs
StatePublished - 2023
Event2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 - Detroit, United States
Duration: Jun 21 2023Jun 23 2023

Publication series

Name2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023

Conference

Conference2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
Country/TerritoryUnited States
CityDetroit
Period06/21/2306/23/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

This work was funded by the U.S. Department of Energy, Office of Vehicle Technology under contract number DE-AC05-00OR22725. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
Office of Vehicle TechnologyDE-AC05-00OR22725
U.S. Department of Energy

    Keywords

    • EV charging
    • automation
    • data analytics
    • electric vehicles
    • resilience

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