典型文献
Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning
文献摘要：
Dear editor,
This letter presents battery full life cycle management and health prognosis based on cloud service and broad learning.Specifically,a cloud-based framework for battery full life cycle management is presented.Then,the broad learning method is proposed for battery state-of-health(SOH)prediction.The features of charging data including the constant current time,constant voltage time,and the total charging time are selected as the input characteristics of the network to estimate SOH.Moreover,the empirical mode decomposition is carried out on the initial data to restore the most essential attenuation trajectory of battery capacity.Experimental results show that the proposed method can provide more accurate battery SOH prediction than several state-of-the-art methods.
文献关键词：
中图分类号：
作者姓名：
Yujie Wang;Kaiquan Li;Zonghai Chen
作者机构：
Department of Automation,University of Science and Technology of China,Hefei 230027,China
文献出处：
引用格式：
[1]Yujie Wang;Kaiquan Li;Zonghai Chen-.Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning)[J].自动化学报（英文版）,2022(08):1540-1542
A类：
B类：
Battery,Full,Life,Cycle,Management,Health,Prognosis,Based,Cloud,Service,Broad,Learning,Dear,editor,
This,letter,presents,battery,full,life,cycle,management,health,prognosis,cloud,service,broad,learning,Specifically,framework,presented,Then,proposed,state,SOH,prediction,features,charging,data,including,constant,current,voltage,total,are,selected,input,characteristics,network,estimate,Moreover,empirical,mode,decomposition,carried,out,initial,restore,most,essential,attenuation,trajectory,capacity,Experimental,results,show,that,can,provide,more,accurate,than,several,art,methods
AB值：
0.650255
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