AI辅助电池材料表征与数据分析  

AI-assisted battery material characterization and data analysis

作　　者：邢瑞鹤 翁素婷 李叶晶 张佳怡 张浩[4] 王雪锋[1,2,3] XING Ruihe;WENG Suting;LI Yejing;ZHANG Jiayi;ZHANG Hao;WANG Xuefeng(Institute of Physics,Chinese Academy of Science,Beijing 110190,China;College of Materials Science and Opto-Electronic Technology,University of Chinese Academy of Sciences,Beijing 100049,China;School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;Beijing Key Laboratory of Advanced Chemical Energy Storage Technologies and Materials,Research Institute of Chemical Defense,Beijing 102205,China)

机构地区：[1]中国科学院物理研究所,北京100190 [2]中国科学院大学材料科学与光电技术学院,北京100049 [3]中国科学院大学物理科学学院,北京100049 [4]防化研究院先进化学蓄电技术与材料北京市重点实验室,北京102205

出　　处：《储能科学与技术》2024年第9期2839-2863,共25页Energy Storage Science and Technology

基　　金：国家重点研发计划(2022YFB2502200);北京市自然科学基金(Z200013);国家自然科学基金项目(52172257);中国博士后科学基金(2023M743739);中国博士后科学基金国家资助博士后研究人员计划(GZC20232939)。

摘　　要：随着锂离子电池(LIBs)的快速发展,传统实验方法在处理复杂数据和优化设计时面临挑战。近年来,人工智能(AI)技术在数据处理、模式识别和预测分析方面展现出巨大潜力,为LIBs的研发提供了新的解决方案。本文综述了AI在锂离子电池材料表征中的应用,包括谱学和成像表征技术。AI通过特征提取和数据分析,提高了谱学分析的准确性和效率;结合先进成像技术,研究者能够以前所未有的精度和速度探索材料内部结构。AI在图像识别、分类和分割中的应用,进一步提升了数据处理的效率和准确性。未来,AI将通过技术创新和跨学科合作,在电池材料科学领域发挥重要作用,推动高性能电池的研发和应用。With the rapid development of commercial lithium-ion batteries(LIBs),traditional experimental methods face challenges in handling complex data and optimizing designs.Recently,artificial intelligence(AI)technology has shown great potential in data processing,pattern recognition,and predictive analysis,providing new solutions for the research and development of LIBs.This paper reviews the application of AI in the characterization of LIB materials,including spectroscopic and imaging techniques.AI improves the accuracy and efficiency of spectroscopic analysis through feature extraction and data analysis.Combined with advanced imaging techniques,researchers can now explore the microstructure of materials with unprecedented precision and speed using AI.AI applications in image recognition,classification,and segmentation further enhance data processing efficiency and accuracy.In the future,AI will play a crucial role in the battery community through technological innovation and interdisciplinary collaboration,driving the development and application of high-performance batteries.

关 键 词：机器学习 锂离子电池 表征 数据分析 

分 类 号：TM911[电气工程—电力电子与电力传动]