AI辅助电池材料表征与数据分析

doi:10.19799/j.cnki.2095-4239.2024.0585

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (9): 2839-2863.doi: 10.19799/j.cnki.2095-4239.2024.0585

• AI辅助先进电池设计与应用专刊 • 下一篇

AI辅助电池材料表征与数据分析

邢瑞鹤¹^,²(), 翁素婷¹, 李叶晶¹, 张佳怡¹^,², 张浩⁴, 王雪锋¹^,²^,³()

^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

收稿日期:2024-06-27 修回日期:2024-07-17 出版日期:2024-09-28 发布日期:2024-09-20
通讯作者: 王雪锋 E-mail:rhxing0322@gmail.com;wxf@iphy.ac.cn
作者简介:邢瑞鹤（2002—），男，硕士研究生，研究方向为基于冷冻电镜电池材料的结构表征和机理探索，E-mail：rhxing0322@gmail.com；
基金资助:
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)

AI-assisted battery material characterization and data analysis

Ruihe XING¹^,²(), Suting WENG¹, Yejing LI¹, Jiayi ZHANG¹^,², Hao ZHANG⁴, Xuefeng WANG¹^,²^,³()

^1.Institute of Physics, Chinese Academy of Science, Beijing 110190, China
^2.College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
^3.School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
^4.Beijing Key Laboratory of Advanced Chemical Energy Storage Technologies and Materials, Research Institute of Chemical Defense, Beijing 102205, China

Received:2024-06-27 Revised:2024-07-17 Online:2024-09-28 Published:2024-09-20
Contact: Xuefeng WANG E-mail:rhxing0322@gmail.com;wxf@iphy.ac.cn

摘要/Abstract

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。

关键词: 机器学习, 锂离子电池, 表征, 数据分析

Abstract:

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.

Key words: machine learning, lithium-ion battery, characterization, data analysis

中图分类号:

TM 911

邢瑞鹤, 翁素婷, 李叶晶, 张佳怡, 张浩, 王雪锋. AI辅助电池材料表征与数据分析[J]. 储能科学与技术, 2024, 13(9): 2839-2863.

Ruihe XING, Suting WENG, Yejing LI, Jiayi ZHANG, Hao ZHANG, Xuefeng WANG. AI-assisted battery material characterization and data analysis[J]. Energy Storage Science and Technology, 2024, 13(9): 2839-2863.

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机器学习模型	特点或应用实例	相关文献
卷积神经网络(CNN)	适合于图像模式识别，应用于管理大量表征数据以快速和自动识别材料组成和相图	[44-48]
人工神经网络(ANN)	用于对大量晶体结构的研究，应用于原子构型采样和学习物理电位	[49-51]
深度神经网络(DNN)	适合非线性函数拟合特征提取，可用于学习编码器和解码器及预测电极特性	[52-53]
递归神经网络(RNN)	深度学习主要框架之一，主要是将上一次迭代的输出作为当前迭代的输入，可用于电池状态预测	[54-55]
自动编码器(AE)	通过逐层的无监督学习先将输入数据进行表征的压缩，应用于降维和特征提取	[52,56]
生成式对抗网络(GAN)	生成器按要求生成样本并通过鉴别器来区分真假，迫使生成器生成更真实的样本，应用于实现X射线断层扫描数据和实验图像的重建	[57-61]
支持向量机(SVM)	能够处理非线性动态问题中非线性特征的相互作用，提高泛化能力	[62-64]
长短期记忆网络(LSTM)	适合于与时间相关的数据，常用于电池单元状态估计	[65-67]

谱学种类	谱学表征方法	功能简介	应用实例简介
衍射谱	X射线衍射(XRD)	确定材料的晶体结构、相组成、晶粒尺寸和应力应变状态	研究电极材料在充放电过程中的相变行为
	电子衍射(ED)	提供材料的高分辨率的晶体结构和原子排列信息	分析材料的晶格缺陷和界面结构
	中子衍射(ND)	对轻元素(如锂、氢等)更敏感，提供轨迹信息	研究充放电过程中的嵌入和脱出机制

振动谱	傅里叶变换红外光谱(FT-IR)	提供化学键的形成和断裂、表面官能团的变化、界面反应产物的信息	分析电极材料的组成、界面反应及电解液的分解产物
振动谱	拉曼光谱(Raman spectrocopy)	检测材料中的化学键和分子振动	识别结构变化和反应产物，电解液溶剂化结构

能谱	飞行时间二次离子质谱(ToF-SIMS)	分析样品表面释放的离子，提供化学组成和三维分布信息	分析材料中各层界面的化学成分和结构
	X射线光电子能谱(XPS)	提供样品表面的元素组成、化学态和相对浓度等信息	分析化合物的化学态、研究电极表面在不同充放电状态下的变化
	电子能量损失谱(EELS)	提供局部的化学信息和电子结构	常与透射电子显微镜(TEM)结合使用，分析材料中的元素分布和氧化态
	X射线吸收谱(XAS)	提供材料的电子结构和局部环境变化	锂和钠离子电池正极材料中的氧化还原反应和结构演变
	X射线荧光光谱(XRF)	提供高精度的元素分布信息	测量电极内部溶液相浓度梯度等
	能量弥散X射线谱(EDS)	确定元素类型与浓度	识别元素种类，分析元素含量和分布

其他	核磁共振(NMR)	化学状态分析	分析材料中的化学环境和结构，可以研究电解液中溶剂和添加剂的分子结构和相互作用

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AI辅助电池材料表征与数据分析

AI-assisted battery material characterization and data analysis

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