A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization

Xiao, Jinhua; Jiang, Chengran; Wang, Bo

doi:10.3390/batteries9010057

Cited by 29 publications

(15 citation statements)

References 153 publications

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“…In the article above, Yildiz and Wo¨rgo¨tter 22 and Li et al 23 used the R-CNN algorithm to recognize bolts, while Li et al 25 used the YOLO algorithm. Based on this, this article selects the R-CNN and YOLO algorithms with exceptional performance in object detection: Mask R-CNN, Faster R-CNN, YOLOv5 and YOLOv8.…”

Section: Deep Learning-based Object Detection Methodsmentioning

confidence: 99%

“…Li et al 25 primarily investigated the detection of hexagon bolts and hexagon nuts. In addition, Zhang et al 26 conducted experiments involving cross bolts and star bolts.…”

Section: Analysis Of Retired Power Battery Shell Boltsmentioning

confidence: 99%

“…Intel's D435 camera was used to collect the images. Some researchers 23,25 use an eye-to-hand approach to capture images, which can provide higher accuracy for subsequent disassembly tasks. Other researchers 26 use an eye-in-hand approach to capture images, which offers a wider field of view.…”

Section: Analysis Of Retired Power Battery Shell Boltsmentioning

confidence: 99%

“…However, the retired power battery shells are prone to wear and tear, which compromises the integrity of the smart tags. Li et al 25 utilized a robot adaptive system and an improved YOLOX model to effectively identify the hexagonal bolts inside the battery components.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent detection approach for end-of-life power battery shell bolts

Li,

Chen,

2024

Advances in Mechanical Engineering

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With the rapid growth of the new energy vehicle industry, the number of end-of-life power batteries, which serve as the technological core, is also increasing significantly. Unfortunately, this rise in retired power batteries has led to severe environmental pollution and resource wastage. The detection of shell bolts in power batteries has thus become a crucial step in the recycling and disassembly process. To address this issue, this research proposes a detection method for end-of-life power battery shell bolts. Based on market analysis, the target bolt for the retired power battery shell was identified. The bolt images were collected and preprocessed to create a custom dataset on the experimental platform. Four popular object detection algorithms were compared, and the YOLOv8 model is selected to improve with EMA module. The improved YOLOv8 model achieves 98.9% for mAP_0.5, which increases more than 2 percentage points. Based on the repeatability of bolt recognition, this detection method can be used for the identification of bolts in other battery shells, providing a theoretical foundation for promoting the robotic disassembly of battery shells.

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Section: Deep Learning-based Object Detection Methodsmentioning

confidence: 99%

“…Li et al 25 primarily investigated the detection of hexagon bolts and hexagon nuts. In addition, Zhang et al 26 conducted experiments involving cross bolts and star bolts.…”

Section: Analysis Of Retired Power Battery Shell Boltsmentioning

confidence: 99%

Section: Analysis Of Retired Power Battery Shell Boltsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An intelligent detection approach for end-of-life power battery shell bolts

Li,

Chen,

2024

Advances in Mechanical Engineering

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“…Some scholars detect collisions by setting sensors on robots 8 . They want to reduce the interaction force between humans and robots by adjusting motor torque or force control 9,10 . Although these methods can reduce collision forces, they cannot prevent collisions.…”

Section: Introductionmentioning

confidence: 99%

Exploring human intention recognition based on human robot collaboration manufacturing toward safety production

Wang,

Xiao

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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With the widespread application of collaborative robots in automated production, it is necessary to improve human safety that has become a noteworthy issue in human-robot collaboration (HRC) manufacturing process. This paper proposes a human-robot collaborative security system based on human intention recognition. Firstly, this system collects collaborative environment information through a camera and inputs the collected information into a data-driven platform. Secondly, action recognition algorithms and facial feature recognition algorithms for human detections are used to identify the angles of human actions and faces in the collaborative environment. Then, human intentions are identified by combining facial angle and action recognition results, which can obtain intention information into the control system. Finally, the security system conducts the robot to respond accordingly based on the obtained intention recognition results, which can improve the safety of human-robot collaboration and ensure high efficiency under safety production.

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Rapid Regeneration of Graphite Anodes via Self‐Induced Microwave Plasma

Shan,

Xu,

Cao

et al. 2024

Adv Funct Materials

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Battery recycling is a promising approach to mitigate the safety, environmental, and economic threats posed by numerous discarded lithium‐ion batteries (LIBs). However, the unclear atomic‐scale degradation of spent graphite complicates recycling, resulting in energy‐intensive impurity removal and graphitization, which hampers industrialization. This study uses Cryo‐transmission electron microscopy (Cryo‐TEM) to characterize spent graphite degradation and develop a scalable graphite self‐induced microwave plasma method for efficient regeneration. Cryo‐TEM images show graphite coated with a solid electrolyte interphase (SEI) layer, revealing lattice defects and structure expansion near the surface that impair electrochemical performance. The self‐induced microwave plasma method eradicates the SEI layer and restores the graphite lattice structure within 30 s. Multiphysics simulations indicate that the microwave field generates a strong electric field on the graphite surface, causing plasma discharge and rapid surface heating. Regenerated graphite demonstrates excellent electrochemical performance, with a specific charge capacity of 352.2 mAh g−1 at 0.2 C and ≈81% capacity retention after 400 cycles, matching commercially available materials. This efficient method offers a promising approach for recycling graphite anodes.

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A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization

Cited by 29 publications

References 153 publications

An intelligent detection approach for end-of-life power battery shell bolts

An intelligent detection approach for end-of-life power battery shell bolts

Exploring human intention recognition based on human robot collaboration manufacturing toward safety production

Rapid Regeneration of Graphite Anodes via Self‐Induced Microwave Plasma

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