An Intelligent Decision Support System for Quality Control in Energy Storage Manufacturing

Abstract

The rapid expansion of the global energy storage market necessitates a paradigm shift in manufacturing quality control, moving from reactive inspection to proactive, intelligent intervention. This review paper explores the development and implementation of Intelligent Decision Support Systems (IDSS) within the context of energy storage manufacturing, specifically focusing on lithium-ion battery production and next-generation storage technologies. The complexity of electrochemical cell assembly, characterized by multi-stage chemical and mechanical processes, requires a robust framework capable of handling high-dimensional data and providing real-time actionable insights. This paper systematically analyzes the architectural components of IDSS, including multi-modal data acquisition layers, advanced machine learning inference engines, and automated decision-making protocols. By synthesizing current theoretical frameworks, the study highlights how the integration of deep learning and reinforcement learning optimizes electrode coating uniformity, cell sealing integrity, and formation process efficiency. Furthermore, a comparative analysis is conducted between traditional statistical process control and modern AI-driven methodologies, demonstrating significant improvements in defect detection rates and waste reduction. The discussion extends to the critical challenges facing the industry, such as data heterogeneity, the 'black box' nature of neural networks, and the computational costs of edge-to-cloud synchronization. Finally, the paper outlines future research trajectories, emphasizing the transition toward self-optimizing 'lights-out' factories and the role of digital twins in predictive quality assurance. This comprehensive review serves as a theoretical foundation for engineers and researchers aiming to enhance the reliability and sustainability of energy storage systems through intelligent automation.