Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning
|更新时间:2026-03-24
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Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning
Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning
Energy Internet2025年2卷第2期
作者机构:
1. College of Artificial Intelligence, Shenyang University of Technology,Shenyang,China
2. Guangzhou Industrial Intelligence Research Institute,Guangzhou,China
Wenhong Wang, Xiangpu Meng, Shuo Li, 等. Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning[J]. Energy Internet, 2025,2(2).
Wenhong Wang, Xiangpu Meng, Shuo Li, et al. Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning[J]. Energy Internet, 2025, 2(2).
Wenhong Wang, Xiangpu Meng, Shuo Li, 等. Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning[J]. Energy Internet, 2025,2(2). DOI: 10.1049/ein2.70004.
Wenhong Wang, Xiangpu Meng, Shuo Li, et al. Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning[J]. Energy Internet, 2025, 2(2). DOI: 10.1049/ein2.70004.
Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning
摘要
Abstract
Driven by green energy advancements and system integration
the surge in retired lithium-ion batteries from electric vehicles has intensified battery recycling challenges. Traditional crushing methods suffer from low material purity
limited economic value
and environmental pollution
necessitating advanced
eco-friendly recycling technologies. Fine disassembly
an emerging low-carbon approach
enhances resource recovery but faces stability and safety challenges due to the diversity of power batteries. This study innovatively applies deep learning to fine disassembly
employing the YOLOv8s model for precise cell localisation and counting
an LSTM-thermal infrared mechanism for fire risk prediction during cutting
and image segmentation for optimised electrode winding control. These methods address battery complexity
offering intelligent
safe solutions for disassembly
improving recycling efficiency
and advancing green energy systems and environmental sustainability.