Inline quality grading of commercial lithium-ion battery manufacturing via da-ta-efficient learning and transferable assessment

Abstract

Lithium-ion battery manufacturing involves a complex sequence of tightly coupled processes, making reliable quality grading essential for ensuring cell consistency, production efficiency, and product reliability. However, existing grading paradigms rely heavily on long-cycle testing and dense labeling, resulting in energy consumption, time cost, and limited scalability. Here, we propose the Data-Efficient Learning and Transferable Assessment (DELTA) framework, which combines feature extraction and semi-supervised consistency classification for early quality grading in manufacturing, using cycle-life at end of life (EOL) as the quality evaluation metric. The framework evaluates data from 6 publicly available datasets, encompassing 421 cells with 3 battery chemistries, 6 charging rates, 6 temperatures, and 8 rated capacities. A Linear Mixed-Effects model extracts static features to quantify material effects, while dynamic features from pre-cycling tests characterize performance stability. A semi-supervised classifier based on Gaussian Mixture Models with an entropy-driven mechanism simulates the absence of true manufacturing labels. Experimental results show that DELTA achieves over 83% classification accuracy of cycle-life at EOL with only 30% labeled data, outperforming state-of-the-art methods such as FixMatch and UDA, while reducing training time by 50%. It maintains more than 95% accuracy on unseen datasets, enabling fast, low-cost, and scalable battery screening in manufacturing, establishing missingness-aware learning as a practical solution for data-limited manufacturing environments. A preliminary scenario analysis suggests that reducing reliance on long-cycle screening could potentially lower testing-related costs and energy consumption in large-scale battery production.