Open AccessOn-line Remaining Charging-discharging Cycle Prediction of Lithium-ion Batteries using Cumulative Indicator
Author(s) -
Zeyu Luo,
Hao Chen,
Xianbo Wang,
Zhi-Xin Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2166/1/012009
Subject(s) - reliability engineering , computer science , battery (electricity) , degradation (telecommunications) , lithium (medication) , line (geometry) , simulation , engineering , mathematics , telecommunications , medicine , power (physics) , physics , geometry , quantum mechanics , endocrinology
Prediction of the effective number of full charging-discharging cycle is valuable for lithium-ion battery (LIB) replacement and recycling. This paper proposes to construct a cumulative degradation indicator (CDI) to work as a more predictable indicator. The proposed CDI is better than the original degradation indicator (DI) in multiple criteria. In the stage of determining the end-of-life (EoL) threshold, a relevance vector machine (RVM) is introduced to screen a small number of available samples, and to reduce the prediction error. In the experimental verification stage, this paper uses LIB full-life data from NASA to verify the early and long-term prediction performance of RCDC using a small sample. The experimental results show that when the proportion of training data approaches 50%, the prediction error gradually converges to the actual value.