Nickel-rich layered oxides(LiNixCoyMnzO2,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,ind...Nickel-rich layered oxides(LiNixCoyMnzO2,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,industrialscale 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.展开更多
基金Ministry of SMEs and Startups,Grant/Award Number:S3248116National Research Foundation of Korea,Grant/Award Numbers:RS-2023-00211636,RS-2024-00416891Ministry of Science and ICT,South Korea,Grant/Award Number:RS-2020-II201336。
文摘Nickel-rich layered oxides(LiNixCoyMnzO2,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,industrialscale 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.
