High quality large-scale nickel-rich layered oxides precursor co-precipitation via domain adaptation-based machine learning

Abstract

Nickel-rich layered oxides (LiNi_xCo_yMn_zO₂, NCM) are among the most promising cathode materials for high-energy lithium-ion batteries, offering high specific capacity and output voltage at a relatively low cost. However, industrial-scale co-precipitation presents significant challenges, particularly in maintaining particle sphericity, ensuring a stable concentration gradient, and preserving production yield when transitioning from lab-scale compositions. This study addresses a critical issue in the large-scale synthesis of nickel-rich NCM (x = 0.8381): nickel leaching, which compromises particle uniformity and battery performance. To mitigate this, we optimize the reaction process and develop an artificial intelligence-driven defect prediction system that enhances precursor stability. Our domain adaptation based machine learning model, which accounts for equipment wear and environmental variations, achieves a defect detection accuracy of 97.8% based on machine data and process conditions. By implementing this approach, we successfully scale up NCM precursor production to over 2 tons, achieving 83% capacity retention after 500 cycles at a 1C rate. In addition, the proposed approach demonstrates the formation of a concentration gradient in the composition and a high sphericity of 0.951 (±0.0796). This work provides new insights into the stable mass production of NCM precursors, ensuring both high yield and performance reliability.

Keywords

domain adaptation / machine learning / mass production / nickel-rich layered oxides cathode / process monitoring / schedule optimization

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