How Pretrained Foundation Models and Cloud-Fog Automation Empower the Recycling of Electrical Vehicles

Siyuan Liu, Dapeng Lan*, Jia Wang, Dongxiao Hu, Zhibo Pang, Honghao Lyu

*Corresponding author for this work

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

Abstract

The increasing prevalence of electric vehicles demands efficient and sustainable management of end-of-life lithium-ion batteries. This paper examines the use of Pretrained Foundation Models and Cloud-Fog Automation to improve robotic disassembly of these batteries. We evaluate the performance of two Vision Transformer Models, in tasks involving deformed, rusty, contaminated, and worn batteries. Our proposed architecture, utilizing cloud and fog computing, balances performance with resource efficiency, providing a scalable solution for electric vehicles battery recycling.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 22nd International Conference on Industrial Informatics, INDIN 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331527471
DOIs
Publication statusPublished - 2024
Event22nd IEEE International Conference on Industrial Informatics, INDIN 2024 - Beijing, China
Duration: 18 Aug 202420 Aug 2024

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576

Conference

Conference22nd IEEE International Conference on Industrial Informatics, INDIN 2024
Country/TerritoryChina
CityBeijing
Period18/08/2420/08/24

Keywords

  • Cloud-Fog Automation
  • Pretrained Foundation Model
  • Remanufacturing
  • Robotic Disassembly

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