南方科技大学知识苑(SUSTech KC): Semi-supervised learning for explainable few-shot battery lifetime prediction

题名	Semi-supervised learning for explainable few-shot battery lifetime prediction
作者	Guo，Nanlin 1; Chen，Sihui2 ; Tao，Jun 1; Liu，Yang 3; Wan，Jiayu 4; Li，Xin 3
通讯作者	Tao，Jun
发表日期	2024-06-19
DOI	10.1016/j.joule.2024.02.020
发表期刊	Joule 影响因子和分区
EISSN	2542-4351
卷号	8 期号:6页码:1820-1836
摘要	Accurate prediction of battery lifetime is critical for ensuring timely maintenance and safety of batteries. Although data-driven methods have made significant progress, their model accuracy is often hampered by a scarcity of labeled data. To address this challenge, we developed a semi-supervised learning technique named partial Bayesian co-training (PBCT), enhancing the modeling of battery lifetime prediction. Leveraging the low-cost unlabeled data, our model extracts hidden information to improve the understanding of the underlying data patterns and achieve higher lifetime prediction accuracy. PBCT outperforms existing approaches by up to 21.9% on lifetime prediction accuracy, with negligible overhead for data acquisition. Moreover, our research suggests that incorporating unlabeled data into the training process can help to uncover critical factors that impact battery lifetime, which may be overlooked with a limited number of labeled data alone. The proposed semi-supervised approach sheds light on the future direction for efficient and explainable data-driven battery status estimation.
关键词	battery lifetime prediction explainability lithium-ion batteries partial Bayesian co-training semi-supervised learning
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
Scopus记录号	2-s2.0-85188995964
来源库	Scopus
引用统计	被引频次[WOS]：9
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/778701
专题	工学院_机械与能源工程系
作者单位	1.State Key Laboratory of Integrated Chips and Systems,School of Microelectronics,Fudan University,Shanghai,200433,China 2.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,518055,China 3.Data Science Research Center,Duke Kunshan University,Kunshan,No. 8 Duke Avenue, Jiangsu Province,215316,China 4.Global Institute of Future Technology,Shanghai Jiao Tong University,Shanghai,No. 800 Dongchuan Road,200240,China
推荐引用方式 GB/T 7714	Guo，Nanlin,Chen，Sihui,Tao，Jun,et al. Semi-supervised learning for explainable few-shot battery lifetime prediction[J]. Joule,2024,8(6):1820-1836.
APA	Guo，Nanlin,Chen，Sihui,Tao，Jun,Liu，Yang,Wan，Jiayu,&Li，Xin.(2024).Semi-supervised learning for explainable few-shot battery lifetime prediction.Joule,8(6),1820-1836.
MLA	Guo，Nanlin,et al."Semi-supervised learning for explainable few-shot battery lifetime prediction".Joule 8.6(2024):1820-1836.