Han Wang , Yajie Song , Xue Sun , Shengkai Mo , Cong Chen , Jiajun Wang
{"title":"利用深度学习对快速充电电池的锂镀层进行车载原位预警和检测","authors":"Han Wang , Yajie Song , Xue Sun , Shengkai Mo , Cong Chen , Jiajun Wang","doi":"10.1016/j.ensm.2024.103585","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate lithium plating detection and warning are essential for developing safer, longer cycle life, and faster charging batteries. However, it is difficult to in-situ detect lithium plating from the electrochemical signals without the introduction of sensors or reference electrodes. Here, we proposed an online lithium plating detection and warning method based on anode potential construction. By establishing a precise mapping relationship between battery voltage and three-electrode potential through deep learning, we can reconstruct the three-electrode curve of batteries accurately without introducing a reference electrode. So that lithium plating can be detected over the full life cycle of batteries with a positive rate of 99.9%. Furthermore, with the combination of the voltage prediction module, the future anode potential can be predicted and the lithium plating can be warned with a positive rate of 98.7%. Our approach provides a new possibility for the development of fast-charging technology and life extension strategies.</p></div>","PeriodicalId":306,"journal":{"name":"Energy Storage Materials","volume":null,"pages":null},"PeriodicalIF":18.9000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Onboard in-situ warning and detection of Li plating for fast-charging batteries with deep learning\",\"authors\":\"Han Wang , Yajie Song , Xue Sun , Shengkai Mo , Cong Chen , Jiajun Wang\",\"doi\":\"10.1016/j.ensm.2024.103585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate lithium plating detection and warning are essential for developing safer, longer cycle life, and faster charging batteries. However, it is difficult to in-situ detect lithium plating from the electrochemical signals without the introduction of sensors or reference electrodes. Here, we proposed an online lithium plating detection and warning method based on anode potential construction. By establishing a precise mapping relationship between battery voltage and three-electrode potential through deep learning, we can reconstruct the three-electrode curve of batteries accurately without introducing a reference electrode. So that lithium plating can be detected over the full life cycle of batteries with a positive rate of 99.9%. Furthermore, with the combination of the voltage prediction module, the future anode potential can be predicted and the lithium plating can be warned with a positive rate of 98.7%. Our approach provides a new possibility for the development of fast-charging technology and life extension strategies.</p></div>\",\"PeriodicalId\":306,\"journal\":{\"name\":\"Energy Storage Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":18.9000,\"publicationDate\":\"2024-06-20\",\"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/S2405829724004112\",\"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/S2405829724004112","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}
Onboard in-situ warning and detection of Li plating for fast-charging batteries with deep learning
Accurate lithium plating detection and warning are essential for developing safer, longer cycle life, and faster charging batteries. However, it is difficult to in-situ detect lithium plating from the electrochemical signals without the introduction of sensors or reference electrodes. Here, we proposed an online lithium plating detection and warning method based on anode potential construction. By establishing a precise mapping relationship between battery voltage and three-electrode potential through deep learning, we can reconstruct the three-electrode curve of batteries accurately without introducing a reference electrode. So that lithium plating can be detected over the full life cycle of batteries with a positive rate of 99.9%. Furthermore, with the combination of the voltage prediction module, the future anode potential can be predicted and the lithium plating can be warned with a positive rate of 98.7%. Our approach provides a new possibility for the development of fast-charging technology and life extension strategies.
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.