Artificial intelligence-based defect prediction in lithium battery manufacturing: A review in the context of Quality 4.0
DOI:
https://doi.org/10.15282/ijame.23.3.2026.16.1052Keywords:
Industry 4.0, Quality 4.0, lithium-ion batteries, Artificial Intelligence, convolutional neural networks, state-of-health predictionAbstract
Guided by the principles of Industry 4.0, the integration of artificial intelligence (AI), convolution neural networks into the lithium-ion battery manufacturing process for analysis and prediction is a significant advancement in the development of Quality 4.0. This review examines the methods that use AI to predict the state of health and state of charge of lithium-ion batteries. First, an overview of Industry 4.0, lithium-ion batteries, artificial intelligence, and convolutional neural networks is provided, followed by the discussion of key challenges in this field. Subsequently, these challenges are refined and the research objectives of this study are summarized. A matrix analysis of the relevant literature across six dimensions is conducted to identify areas of research that have the greatest value and significance to lithium-ion battery manufacturing. Furthermore, the process of lithium-ion battery manufacturing is introduced, the key steps and critical nodes involved are summarized, and digital and artificial intelligence technologies currently used in the production of lithium-ion batteries are reviewed. Finally, insights are given by responding to the refined research questions on the basis of a comprehensive synthesis of the literature to guide future research and innovation in the field of lithium-ion battery manufacturing and contribute to the high-quality and sustainable development of this field. In total, 200 representative articles published between 2004 and 2025 are analyzed in a 6×6 matrix of research objectives and methods, with approximately 50% of scholars adopting an optimistic view of AI integration and around 25% expressing concerns about its early-stage limitations.
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