Recent Advancements in Artificial Intelligence in Battery Recycling
Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/10/12/440 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850239773134815232 |
|---|---|
| author | Subin Antony Jose Connor Andrew Dennis Cook Joseph Palacios Hyundeok Seo Christian Eduardo Torres Ramirez Jinhong Wu Pradeep L. Menezes |
| author_facet | Subin Antony Jose Connor Andrew Dennis Cook Joseph Palacios Hyundeok Seo Christian Eduardo Torres Ramirez Jinhong Wu Pradeep L. Menezes |
| author_sort | Subin Antony Jose |
| collection | DOAJ |
| description | Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials. |
| format | Article |
| id | doaj-art-8f60b6d574e64418b4a3c42a12412a2e |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-8f60b6d574e64418b4a3c42a12412a2e2025-08-20T02:01:04ZengMDPI AGBatteries2313-01052024-12-01101244010.3390/batteries10120440Recent Advancements in Artificial Intelligence in Battery RecyclingSubin Antony Jose0Connor Andrew Dennis Cook1Joseph Palacios2Hyundeok Seo3Christian Eduardo Torres Ramirez4Jinhong Wu5Pradeep L. Menezes6Department of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USADepartment of Mechanical Engineering, University of Nevada-Reno, Reno, NV 89557, USABattery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, and renewable energy, efficient recycling is essential. Traditional recycling methods, often reliant on manual labor, suffer from inefficiencies and environmental harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments in AI applications for battery recycling, focusing on methodologies, challenges, and future directions. AI technologies, particularly machine learning and deep learning models, are revolutionizing battery sorting, classification, and disassembly processes. AI-powered systems enhance efficiency by automating tasks such as battery identification, material characterization, and robotic disassembly, reducing human error and occupational hazards. Additionally, integrating AI with advanced sensing technologies like computer vision, spectroscopy, and X-ray imaging allows for precise material characterization and real-time monitoring, optimizing recycling strategies and material recovery rates. Despite these advancements, data quality, scalability, and regulatory compliance must be addressed to realize AI’s full potential in battery recycling. Collaborative efforts across interdisciplinary domains are essential to develop robust, scalable AI-driven recycling solutions, paving the way for a sustainable, circular economy in battery materials.https://www.mdpi.com/2313-0105/10/12/440battery recyclingartificial intelligencecomputer visionlithium ion battery |
| spellingShingle | Subin Antony Jose Connor Andrew Dennis Cook Joseph Palacios Hyundeok Seo Christian Eduardo Torres Ramirez Jinhong Wu Pradeep L. Menezes Recent Advancements in Artificial Intelligence in Battery Recycling Batteries battery recycling artificial intelligence computer vision lithium ion battery |
| title | Recent Advancements in Artificial Intelligence in Battery Recycling |
| title_full | Recent Advancements in Artificial Intelligence in Battery Recycling |
| title_fullStr | Recent Advancements in Artificial Intelligence in Battery Recycling |
| title_full_unstemmed | Recent Advancements in Artificial Intelligence in Battery Recycling |
| title_short | Recent Advancements in Artificial Intelligence in Battery Recycling |
| title_sort | recent advancements in artificial intelligence in battery recycling |
| topic | battery recycling artificial intelligence computer vision lithium ion battery |
| url | https://www.mdpi.com/2313-0105/10/12/440 |
| work_keys_str_mv | AT subinantonyjose recentadvancementsinartificialintelligenceinbatteryrecycling AT connorandrewdenniscook recentadvancementsinartificialintelligenceinbatteryrecycling AT josephpalacios recentadvancementsinartificialintelligenceinbatteryrecycling AT hyundeokseo recentadvancementsinartificialintelligenceinbatteryrecycling AT christianeduardotorresramirez recentadvancementsinartificialintelligenceinbatteryrecycling AT jinhongwu recentadvancementsinartificialintelligenceinbatteryrecycling AT pradeeplmenezes recentadvancementsinartificialintelligenceinbatteryrecycling |