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Health-Conscious vehicle battery state estimation based on deep transfer learning

文献类型：期刊论文

作者Li, Shuangqi2,3; He, Hongwen3; Zhao, Pengfei1; Cheng, Shuang2
刊名APPLIED ENERGY
出版日期2022-06-15
卷号316页码:8
关键词Transportation electrification Electric vehicles Battery energy storage Deep transfer learning Battery management system Battery state estimation
ISSN号0306-2619
DOI10.1016/j.apenergy.2022.119120
通讯作者He, Hongwen(hwhebit@bit.edu.cn)
英文摘要Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle.
WOS关键词LITHIUM-ION BATTERY ; MODEL
资助项目National Nature Science Foundation of China[U1864202]
WOS研究方向Energy & Fuels ; Engineering
语种英语
WOS记录号WOS:000793711700001
出版者ELSEVIER SCI LTD
资助机构National Nature Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/49479]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者He, Hongwen
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Bath, Dept Elect & Elect Engn, Bath, Avon, England
3.Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shuangqi,He, Hongwen,Zhao, Pengfei,et al. Health-Conscious vehicle battery state estimation based on deep transfer learning[J]. APPLIED ENERGY,2022,316:8.
APA Li, Shuangqi,He, Hongwen,Zhao, Pengfei,&Cheng, Shuang.(2022).Health-Conscious vehicle battery state estimation based on deep transfer learning.APPLIED ENERGY,316,8.
MLA Li, Shuangqi,et al."Health-Conscious vehicle battery state estimation based on deep transfer learning".APPLIED ENERGY 316(2022):8.

入库方式： OAI收割

来源：自动化研究所

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