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) and 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 convolution neural networks is provided, followed by a discussion of the 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 provided 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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