Haosong Yang , Xueyan Li , Kang Fu , Wenxu Shang , Kai Sun , Zhi Yang , Guojun Hu , Peng Tan
{"title":"Behavioral description of lithium-ion batteries by multiphysics modeling","authors":"Haosong Yang , Xueyan Li , Kang Fu , Wenxu Shang , Kai Sun , Zhi Yang , Guojun Hu , Peng Tan","doi":"10.1016/j.decarb.2024.100076","DOIUrl":null,"url":null,"abstract":"<div><div>Upgrades to power systems and the rapid growth of electric vehicles significantly heighten the importance of lithium-ion batteries (LiBs) in energy systems. As a complex dynamic system, the charging and discharging process of LiBs involves the evolution of multiphysics fields, such as concentration, electricity, and stress. For quantitative analysis of the internal mechanisms of LiBs, as well as the development guidance and performance prediction of high-performance batteries, modeling has advantages that cannot be matched by traditional experimental methods. Major research efforts in the past decades have made significant strides in modeling the internal processes and physical field evolution of LiBs. Importantly, the scattered ideas need to be integrated into a structured framework to form a complete LiBs multi-physical field model. This work reviews important advances in LiBs modeling from the perspectives of describing the internal processes of the battery and portraying the evolution of the physical field. First, quantitative descriptions of the charging and discharging behaviors and the side reactions are reviewed to investigate the battery reaction mechanisms. In addition, the characterization of the evolution of the stress and temperature fields within the battery as well as the coupling between them and the internal reactions are discussed. Finally, some suggestions for future improvements in the modeling are given, ranging from equation optimization to parameter acquisition and the application of artificial intelligence. It is hoped that this work will facilitate the development of models with sufficient accuracy and efficient computational cost to provide guidance for the improvement of LiBs.</div></div>","PeriodicalId":100356,"journal":{"name":"DeCarbon","volume":"6 ","pages":"Article 100076"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DeCarbon","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949881324000428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Upgrades to power systems and the rapid growth of electric vehicles significantly heighten the importance of lithium-ion batteries (LiBs) in energy systems. As a complex dynamic system, the charging and discharging process of LiBs involves the evolution of multiphysics fields, such as concentration, electricity, and stress. For quantitative analysis of the internal mechanisms of LiBs, as well as the development guidance and performance prediction of high-performance batteries, modeling has advantages that cannot be matched by traditional experimental methods. Major research efforts in the past decades have made significant strides in modeling the internal processes and physical field evolution of LiBs. Importantly, the scattered ideas need to be integrated into a structured framework to form a complete LiBs multi-physical field model. This work reviews important advances in LiBs modeling from the perspectives of describing the internal processes of the battery and portraying the evolution of the physical field. First, quantitative descriptions of the charging and discharging behaviors and the side reactions are reviewed to investigate the battery reaction mechanisms. In addition, the characterization of the evolution of the stress and temperature fields within the battery as well as the coupling between them and the internal reactions are discussed. Finally, some suggestions for future improvements in the modeling are given, ranging from equation optimization to parameter acquisition and the application of artificial intelligence. It is hoped that this work will facilitate the development of models with sufficient accuracy and efficient computational cost to provide guidance for the improvement of LiBs.