可充电电池中的人工智能:进步与前景

IF 18.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL Energy Storage Materials Pub Date : 2024-11-01 DOI:10.1016/j.ensm.2024.103860
Yige Xiong , Die Zhang , Xiaorong Ruan , Shanbao Jiang , Xueqin Zou , Wei Yuan , Xiuxue Liu , Yapeng Zhang , Zeqi Nie , Donghai Wei , Yubin Zeng , Peng Cao , Guanhua Zhang
{"title":"可充电电池中的人工智能:进步与前景","authors":"Yige Xiong ,&nbsp;Die Zhang ,&nbsp;Xiaorong Ruan ,&nbsp;Shanbao Jiang ,&nbsp;Xueqin Zou ,&nbsp;Wei Yuan ,&nbsp;Xiuxue Liu ,&nbsp;Yapeng Zhang ,&nbsp;Zeqi Nie ,&nbsp;Donghai Wei ,&nbsp;Yubin Zeng ,&nbsp;Peng Cao ,&nbsp;Guanhua Zhang","doi":"10.1016/j.ensm.2024.103860","DOIUrl":null,"url":null,"abstract":"<div><div>Advanced rechargeable battery technologies are the primary source of energy storage, which hold significant promise for tackling energy challenges. However, the progress of these technologies is affected by various factors, including technical and capital investment challenges. The technical challenges primarily involve performance optimization. Artificial intelligence (AI), with its robust data processing and decision-making capabilities, is poised to promote the high-quality and rapid development of rechargeable battery research. This paper begins by elucidating the key techniques and fundamental framework of AI, then summarizes applications of AI in advanced battery research. Subsequently, critical applications and exemplary research advancements of AI techniques in various batteries are presented. Finally, potential issues and future development directions of AI technologies in facilitating the development of batteries are discussed. This review offers guidance for applications of AI techniques in future research on advanced batteries.</div></div>","PeriodicalId":306,"journal":{"name":"Energy Storage Materials","volume":"73 ","pages":"Article 103860"},"PeriodicalIF":18.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in rechargeable battery: Advancements and prospects\",\"authors\":\"Yige Xiong ,&nbsp;Die Zhang ,&nbsp;Xiaorong Ruan ,&nbsp;Shanbao Jiang ,&nbsp;Xueqin Zou ,&nbsp;Wei Yuan ,&nbsp;Xiuxue Liu ,&nbsp;Yapeng Zhang ,&nbsp;Zeqi Nie ,&nbsp;Donghai Wei ,&nbsp;Yubin Zeng ,&nbsp;Peng Cao ,&nbsp;Guanhua Zhang\",\"doi\":\"10.1016/j.ensm.2024.103860\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Advanced rechargeable battery technologies are the primary source of energy storage, which hold significant promise for tackling energy challenges. However, the progress of these technologies is affected by various factors, including technical and capital investment challenges. The technical challenges primarily involve performance optimization. Artificial intelligence (AI), with its robust data processing and decision-making capabilities, is poised to promote the high-quality and rapid development of rechargeable battery research. This paper begins by elucidating the key techniques and fundamental framework of AI, then summarizes applications of AI in advanced battery research. Subsequently, critical applications and exemplary research advancements of AI techniques in various batteries are presented. Finally, potential issues and future development directions of AI technologies in facilitating the development of batteries are discussed. This review offers guidance for applications of AI techniques in future research on advanced batteries.</div></div>\",\"PeriodicalId\":306,\"journal\":{\"name\":\"Energy Storage Materials\",\"volume\":\"73 \",\"pages\":\"Article 103860\"},\"PeriodicalIF\":18.9000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Storage Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S240582972400686X\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Storage Materials","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240582972400686X","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

先进的可充电电池技术是能源储存的主要来源,在应对能源挑战方面大有可为。然而,这些技术的进展受到各种因素的影响,包括技术和资本投资方面的挑战。技术挑战主要涉及性能优化。人工智能(AI)具有强大的数据处理和决策能力,有望促进充电电池研究的高质量和快速发展。本文首先阐明了人工智能的关键技术和基本框架,然后总结了人工智能在先进电池研究中的应用。随后,介绍了人工智能技术在各种电池中的关键应用和示范性研究进展。最后,讨论了人工智能技术在促进电池发展方面的潜在问题和未来发展方向。本综述为人工智能技术在未来先进电池研究中的应用提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Artificial intelligence in rechargeable battery: Advancements and prospects
Advanced rechargeable battery technologies are the primary source of energy storage, which hold significant promise for tackling energy challenges. However, the progress of these technologies is affected by various factors, including technical and capital investment challenges. The technical challenges primarily involve performance optimization. Artificial intelligence (AI), with its robust data processing and decision-making capabilities, is poised to promote the high-quality and rapid development of rechargeable battery research. This paper begins by elucidating the key techniques and fundamental framework of AI, then summarizes applications of AI in advanced battery research. Subsequently, critical applications and exemplary research advancements of AI techniques in various batteries are presented. Finally, potential issues and future development directions of AI technologies in facilitating the development of batteries are discussed. This review offers guidance for applications of AI techniques in future research on advanced batteries.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy Storage Materials
Energy Storage Materials Materials Science-General Materials Science
CiteScore
33.00
自引率
5.90%
发文量
652
审稿时长
27 days
期刊介绍: Energy Storage Materials is a global interdisciplinary journal dedicated to sharing scientific and technological advancements in materials and devices for advanced energy storage and related energy conversion, such as in metal-O2 batteries. The journal features comprehensive research articles, including full papers and short communications, as well as authoritative feature articles and reviews by leading experts in the field. Energy Storage Materials covers a wide range of topics, including the synthesis, fabrication, structure, properties, performance, and technological applications of energy storage materials. Additionally, the journal explores strategies, policies, and developments in the field of energy storage materials and devices for sustainable energy. Published papers are selected based on their scientific and technological significance, their ability to provide valuable new knowledge, and their relevance to the international research community.
期刊最新文献
Optimized molecular interactions significantly enhance capacitive energy storage in polymer blends at 150 °C A High Power Flexible Zn-Air Battery via Concurrent PAA Modulation and Structural Tuning Surface acidity regulation for boosting Li2O2 decomposition towards lower charge overpotential Li–O2 batteries “Preferential Adsorption-Decomposition and Strong Binding” Strategy-Derived Interphase Enabling Fast-Charging and Wide-Temperature Sodium Metal Batteries Unlocking Advanced Sodium Storage Performance: High-Entropy Modulates Crystallographic Sites with Reversible Multi-Electron Reaction
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1