干旱区生态环境知识资源中心(Arid): An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries

Arid

DOI	10.1016/j.compchemeng.2023.108275
	An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries
	Bhadriraju, Bhavana; Kwon, Joseph Sang-Il; Khan, Faisal
通讯作者	Kwon, JSI
来源期刊	COMPUTERS & CHEMICAL ENGINEERING
ISSN	0098-1354
EISSN	1873-4375
出版年	2023
卷号	175
英文摘要	During the multi-cycle operation of a Li-ion battery, its process dynamics evolve in two distinct timescales: slow degradation dynamics over multiple cycles and fast cycling dynamics during each cycle. The slow inter-cyclic dynamics of capacity degradation describes remaining useful life (RUL), and the fast intra-cyclic dynamics of state of charge (SoC) and voltage provides an insight into available power, temperature change, and charge and discharge times. Hence, predicting both intra and inter-cyclic dynamics aids in understanding battery degradation and assessing its performance. To this end, we develop a data-driven approach to model both fast and slow degradation dynamics using operable adaptive sparse identification of systems (OASIS). Specifically, the developed method determines two battery models: inter-OASIS and intra-OASIS. The inter-OASIS model predicts capacity degradation and estimates RUL, and utilizing this prediction, the intra-OASIS model accurately predicts SoC and voltage dynamics. The developed method is demonstrated on a LiFePO4/graphite battery system.
英文关键词	Remaining useful life Li-ion battery Sparse regression Deep learning Real-time prediction Adaptive modeling
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001001548700001
WOS关键词	SINGLE-PARTICLE MODEL ; SPARSE IDENTIFICATION ; MULTISCALE SIMULATION ; STATE ; CHARGE ; DEGRADATION ; REGRESSION ; FRAMEWORK ; PROGNOSTICS ; PARAMETER
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Chemical
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395811
推荐引用方式 GB/T 7714	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,Khan, Faisal. An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries[J],2023,175.
APA	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,&Khan, Faisal.(2023).An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries.COMPUTERS & CHEMICAL ENGINEERING,175.
MLA	Bhadriraju, Bhavana,et al."An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries".COMPUTERS & CHEMICAL ENGINEERING 175(2023).