An integrated imputation-prediction scheme for prognostics of battery data with missing observations
作者：
Highlights：
• An integrated scheme is proposed for prognostics of Lithium-ion batteries.
• The prognostic scheme contains pre-processing and prediction modules.
• Novel imputation techniques handle battery data with missing observations.
• The proposed multiple imputation technique reveals the uncertainty of estimations.
• Extreme learning machines predict the remaining useful life over a long horizon.
摘要
•An integrated scheme is proposed for prognostics of Lithium-ion batteries.•The prognostic scheme contains pre-processing and prediction modules.•Novel imputation techniques handle battery data with missing observations.•The proposed multiple imputation technique reveals the uncertainty of estimations.•Extreme learning machines predict the remaining useful life over a long horizon.
论文关键词：Lithium-ion batteries,Prognostics and health management,Remaining useful life,Incomplete scenarios,Missing data imputation,Extreme learning machines
论文评审过程：Received 10 June 2017, Revised 15 August 2018, Accepted 16 August 2018, Available online 22 August 2018, Version of Record 18 September 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.08.033
