Battery health state prediction based on lightweight neural networks: A review

doi:10.1007/s11581-024-05857-y

	Battery health state prediction based on lightweight neural networks: A review
	Zhang, Longlong 1,3; Wang, Shanshuai 2; Wang, Shi 3; Zhong, Bai 4; Li, Zhaoting 5; Wang, Licheng 6; Wang, Kai 1,2,7
	2024-10-05
发表期刊	IONICS
ISSN	0947-7047
页码	27
摘要	Due to their superior properties, lithium-ion batteries (LIBs) have become the primary energy storage medium for electric vehicles (EVs), driven by widespread adoption. Nevertheless, a significant barrier hindering EV uptake lies in accurately assessing power LIBs' health status and lifespan under prolonged demanding conditions. The neural network-based prediction method can increase the model's prediction accuracy. However, because of the model's complexity and abundance of features, current data-driven prediction technology frequently requires a lot of processing power to predict the battery's health state. This study discusses recent approaches to life prediction using lightweight neural networks, with an emphasis on the aforementioned issues. The LIB's aging mechanism and state of health (SOH) definition are first explained. A number of neural network models are then presented, followed by a summary of the available lightweight neural network prediction techniques and the machine learning framework for the prediction model, which aims to produce a more flexible and accurate model. This research provides references for predicting the health condition of LIBs and posits that in the future, more creative lightweight neural network models will become the standard in SOH prediction.
关键词	State of health Lithium-ion batteries Lightweight neural networks Attentional mechanisms
DOI	10.1007/s11581-024-05857-y
收录类别	SCI
语种	英语
资助项目	Youth Innovation Technology Project of Higher School in Shandong Province[2022KJ139] ; Zhejiang Province Natural Science Foundation[LY22E070007] ; National Natural Science Foundation of China[52007170]
WOS研究方向	Chemistry ; Electrochemistry ; Physics
WOS类目	Chemistry, Physical ; Electrochemistry ; Physics, Condensed Matter
WOS记录号	WOS:001326016300001
出版者	SPRINGER HEIDELBERG
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39554
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Kai
作者单位	1.Qingdao Univ, Weihai Innovat Res Inst, Sch Elect Engn, Qingdao 266000, Peoples R China 2.Dalian Jiaotong Univ, Sch Mech Engn, Dalian 116028, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Alibaba Cloud Comp Co Ltd, Beijing 311305, Peoples R China 5.Brown Univ, Sch Engn, Providence, RI 02912 USA 6.Zhejiang Univ Technol, Sch Informat Engn, Hangzhou 310014, Peoples R China 7.Shandong Suoxiang Intelligent Technol Co Ltd, Weifang 261101, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Longlong,Wang, Shanshuai,Wang, Shi,et al. Battery health state prediction based on lightweight neural networks: A review[J]. IONICS,2024:27.
APA	Zhang, Longlong.,Wang, Shanshuai.,Wang, Shi.,Zhong, Bai.,Li, Zhaoting.,...&Wang, Kai.(2024).Battery health state prediction based on lightweight neural networks: A review.IONICS,27.
MLA	Zhang, Longlong,et al."Battery health state prediction based on lightweight neural networks: A review".IONICS (2024):27.