Data-Driven Defect Reduction in Energy Storage System Manufacturing: A Review of Predictive Quality Control and Intelligent Inspection Technologies
DOI:
https://doi.org/10.71222/rnms7975Keywords:
Energy Storage Systems, Data-Driven Manufacturing, Defect Reduction, Predictive Quality Control, Machine Learning, Digital Twin, Battery ManufacturingAbstract
The rapid expansion of the global energy storage system (ESS) market has significantly increased the demand for highly reliable and defect-resistant battery manufacturing processes. Conventional quality control approaches, which primarily rely on end-of-line inspection and statistical sampling, are increasingly insufficient for high-energy-density lithium-ion battery production due to the complexity and sensitivity of modern manufacturing environments. This paper presents a structured review of data-driven defect reduction strategies in ESS manufacturing, with a particular focus on predictive quality control, multi-modal sensing, machine learning-based inspection, and digital twin integration. Relevant literature published between 2018 and 2025 was systematically reviewed from major academic databases including IEEE Xplore, Scopus, and ScienceDirect. The review synthesizes current developments in real-time electrode coating monitoring, assembly precision prediction, electrochemical signature analysis, and AI-enabled defect classification. Comparative analyses of industrial sensing technologies and machine learning architectures are presented to evaluate their effectiveness in improving manufacturing yield and operational reliability. The paper further proposes an integrated conceptual framework connecting sensor acquisition, predictive analytics, adaptive process control, and digital twin synchronization for intelligent ESS manufacturing. Key challenges including data interoperability, limited cross-factory datasets, model explainability, and deployment scalability are also discussed. The findings indicate that data-driven manufacturing strategies have become essential for achieving high-throughput, high-consistency, and safety-critical battery production in next-generation ESS applications.References
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