Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning

Wenhong Wang; Xiangpu Meng; Shuo Li; Cheng Luo; Zhijia Zhang; Meina Zhai; Mingzhe Yuan; Dunxin Huang

doi:10.1049/ein2.70004

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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

更新时间：2026-03-24

- Multimodal Sensing-Driven Intelligent Disassembly System for Power Batteries: A Collaborative Architecture of Localisation-Early Warning-Control Based on Deep Learning
- Energy Internet Vol. 2, Issue 2, (2025)
- 作者机构：
  
  1. College of Artificial Intelligence, Shenyang University of Technology,Shenyang,China
  2. Guangzhou Industrial Intelligence Research Institute,Guangzhou,China
  3. Contribution: Supervision, Project administration, Resources, Writing - review & editing
  4. Contribution: Conceptualization, Methodology, Validation, Writing - original draft
  5. Contribution: Investigation, Conceptualization, Data curation, Formal analysis, Writing - original draft
  6. College of Information Engineering, Shenyang University of Chemical Technology,Shenyang,China
  7. Contribution: Writing - review & editing, Data curation, Investigation
  8. Contribution: Resources, Supervision, Project administration, Formal analysis
  9. Contribution: Writing - review & editing, Project administration, Resources, Formal analysis
  10. Contribution: Writing - review & editing, Project administration, Resources, Supervision
  11. Contribution: Resources
- 作者简介：
- 基金信息：
- DOI：10.1049/ein2.70004
  CLC：
- Published：2025
- 稿件说明：
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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：

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.

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.

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