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Remaining useful life prediction of lithium‐ion battery using a novel health indicator
Author(s) -
Wang Ranran,
Feng Hailin
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2792
Subject(s) - battery (electricity) , probabilistic logic , lithium ion battery , reliability engineering , relevance vector machine , transformation (genetics) , degradation (telecommunications) , computer science , support vector machine , internal resistance , process (computing) , battery capacity , data mining , artificial intelligence , machine learning , engineering , chemistry , telecommunications , biochemistry , physics , quantum mechanics , gene , power (physics) , operating system
Abstract Remaining useful life (RUL) prediction plays a significant role in the health prognostic of lithium‐ion batteries (LIBs). The capacity or internal resistance is commonly used to quantify degradation process and predict RUL of LIB, but those two indicators are difficult to be obtained due to complex operational conditions and high costs, respectively. To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized by Box‐Cox transformation and evaluated by correlation analysis for degradation modeling accurately. Finally, relevance vector machine (RVM) algorithm is utilized to make a probabilistic prediction for battery RUL based on the extracted HI. The correlation analysis verifies the effectiveness of the novel HI, and comparative experiments demonstrate the proposed method can predict RUL of LIB more accurately.