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

doi:10.1016/j.apenergy.2022.119120

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Health-Conscious vehicle battery state estimation based on deep transfer learning
	Li, Shuangqi 2,3; He, Hongwen 3; Zhao, Pengfei 1; Cheng, Shuang 2
刊名	APPLIED ENERGY
	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
DOI	10.1016/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.
资助项目	National Nature Science Foundation of China[U1864202]
WOS关键词	LITHIUM-ION BATTERY ; MODEL
WOS研究方向	Energy & Fuels ; Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000793711700001
资助机构	National Nature Science Foundation of China
内容类型	期刊论文
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.