The Application of Artificial Intelligence in the Effective Battery Life Cycle in the Closed Circular Economy Model—A Perspective

Abstract

Global pollution of the environment is one of the most challenging environmental problems. Electronic-based population and anthropogenic activity are the main reasons for dramatically increasing the scale of waste generation, particularly battery waste. Improper battery waste disposal causes harmful environmental effects. Due to the release of heavy metals, battery waste affects ecosystems and health. We are faced with the challenge of effective battery waste management, especially recycling, to prevent the depletion of natural resources and maintain ecological balance. Artificial Intelligence (AI) is practically present in all areas of our lives. It enables the reduction of the costs associated with various types of research, increases automation, and accelerates productivity. This paper reviews the representative research progress of effective Artificial Intelligence-based battery waste management in the context of sustainable development, in particular, the analysis of current trends, algorithm accuracy, and data availability. Finally, the future lines of research and development directions of human-oriented Artificial Intelligence applications both in the battery production process and in battery waste management are discussed.

Direct recycling of Li‐ion batteries from cell to pack level: Challenges and prospects on technology, scalability, sustainability, and economics

“…There are three key sections to the autonomous robot disassembly 91 system: the detection of battery pack information, the optimization of the disassembly order, and the actual disassembly process itself. AI-enabled robots 76,220,221 can take over this type of dismantling job. Such automated detection systems can pick batteries suited for recycling, 51,55,222 and the following step is the disassembly process using robots equipped with vision sensors.…”

3D Printing‐Enabled Design and Manufacturing Strategies for Batteries: A Review

“…Computer vision (e.g., convolutional neural networks (CNN), Object Detection algorithms (e.g., YOLO, SSD), Image Classification models (e.g., ResNet, Inception)), robotic systems (e.g., robotic arms, Gantry systems, Delta robotics, automated conveyor systems), and data analytics (e.g., machine learning, deep learning, artificial intelligence) as mentioned above, can assist in the identification, classification, and sorting of battery components and materials for efficient recovery and recycling. [ 253,254 ] For example, image analysis and computer vision tools, including OpenCV (Open Source Computer Vision Library), TensorFlow, PyTorch, MATLAB, Caffe, and scikit‐image as a Python library for image processing and analysis, can assist with battery recycling in pyrometallurgical, hydrometallurgical, and biological recycling methods.…”

The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland

“…As a result, the amount of resource extraction and waste production associated with excessive product development can be reduced substantially. AI can also help in predicting how materials change over time, such as their overall durability and potential toxicities [54]. This type of information can help in advancing the reverse logistics and maintenance of products.…”