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 language | English |
|---|---|
| Title of host publication | Proceedings - 2024 IEEE 22nd International Conference on Industrial Informatics, INDIN 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331527471 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 22nd IEEE International Conference on Industrial Informatics, INDIN 2024 - Beijing, China Duration: 18 Aug 2024 → 20 Aug 2024 |
Publication series
| Name | IEEE International Conference on Industrial Informatics (INDIN) |
|---|---|
| ISSN (Print) | 1935-4576 |
Conference
| Conference | 22nd IEEE International Conference on Industrial Informatics, INDIN 2024 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 18/08/24 → 20/08/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy -
SDG 8 Decent Work and Economic Growth -
SDG 12 Responsible Consumption and Production
Keywords
- Cloud-Fog Automation
- Pretrained Foundation Model
- Remanufacturing
- Robotic Disassembly
Fingerprint
Dive into the research topics of 'How Pretrained Foundation Models and Cloud-Fog Automation Empower the Recycling of Electrical Vehicles'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver