Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method

doi:10.1016/j.apenergy.2019.113841

CSpace

	Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method
	Zhang, Caiping 1; Wang, Yubin 1; Gao, Yang 1; Wang, Fang 2; Mu, Biqiang 3; Zhang, Weige 1
	2019-12-15
发表期刊	APPLIED ENERGY
ISSN	0306-2619
卷号	256 页码:10
摘要	The requirement for energy density of lithium-ion batteries becomes more urgent due to the rising demand for driving range of electric vehicles in recent years. Meanwhile, the performance stability of batteries with high energy densities tends to deteriorate, leading to accelerating degradation and safety issues. As a result, it is critical to explore the reasons that yield the sudden degradation and to recognize the degradation knee point of Nickel-Cobalt-Manganese batteries commonly used for electric vehicles. Existing results have disclosed that the lithium deposition of negative electrode dominates the sudden degradation of battery capacity. This paper extracts key parameters that characterize the aging status to facilitate knee point recognition in engineering practice. Furthermore, a novel method that integrates quantile regression and Monte Carlo simulation method to identify the accelerated fading knee point is introduced. The dynamic safety boundary determination method for the whole battery lifetime is proposed to update and monitor the safety zone. It is verified by experiments that the recognition results of capacity degradation knee point appear within 90-95% capacity range at 25 degrees C, 35 degrees C and 45 degrees C conditions, which can provide an early warning before the battery fails. Using the proposed method for recognizing the sudden degradation of capacity, recognition result is effective even if the input is disturbed and has strong reliability and stability under different conditions. It is helpful to promote the sustainable and stable development of the electric vehicles and improve advanced applied energy technologies.
关键词	Nickel-Cobalt-Manganese lithium-ion battery Accelerated aging Sudden degradation Recognition Quantile regression
DOI	10.1016/j.apenergy.2019.113841
收录类别	SCI
语种	英语
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:000497981300012
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50348
专题	中国科学院数学与系统科学研究院
通讯作者	Wang, Yubin; Wang, Fang
作者单位	1.Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr NANTEC, Beijing 100044, Peoples R China 2.China Automot Technol & Res Ctr Co Ltd CATARC, Tianjin Key Lab Evaluat Technol Elect Vehicles, Tianjin 300300, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Caiping,Wang, Yubin,Gao, Yang,et al. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method[J]. APPLIED ENERGY,2019,256:10.
APA	Zhang, Caiping,Wang, Yubin,Gao, Yang,Wang, Fang,Mu, Biqiang,&Zhang, Weige.(2019).Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method.APPLIED ENERGY,256,10.
MLA	Zhang, Caiping,et al."Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method".APPLIED ENERGY 256(2019):10.