A Deep Learning-Based Classification Model of Lithium-Ion Battery Components for Automated Recycling
DOI:
https://doi.org/10.62411/faith.3048-3719-262Keywords:
Automated recycling, Battery waste classification, Deep Learning, E-waste management, Image annotation, LabelImg, Lithium-ion battery, Object detection, YOLOv8Abstract
Lithium-ion battery demand continues to rise as consumer electronics, hybrid and electric vehicles, and other technologies evolve. This means that numerous lithium-ion batteries are likely to be disposed of, leading to serious disposal problems and negative impacts on the environment and energy conservation. The commonly used lithium-ion battery recycling methods, which are chemical and mechanical, pose challenges, such as some batteries exploding, thermal runaway, or fire. Classification of used lithium-ion battery waste is required to be efficient and reliable. The purpose of this study was to develop a deep learning-based classification model of lithium-ion battery components for automated recycling. A dataset containing images of end-of-life lithium-ion battery components was collected from selected recycling centres in Nairobi, Kenya, and Kaggle.com. The images from selected fields were annotated using the Labelimg annotation tool. The dataset was split into three sets: 70% as the training set, 15% as the validation set, and 15% testing set. A Yolov8n model was then trained using the training set to detect and classify end-of-life lithium-ion battery components. The performance of the model was evaluated using the validation set and test set. The final trained model attained 0.903 precision, 0.792 recall, 0.852 [email protected], 0.724 [email protected], and 0.844 f1-score. The results from this research study could pave the way for innovative battery recycling measures, guaranteeing that valuable resources are reclaimed and toxic battery waste is managed efficiently and safely.
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