Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation

被引:0
|
作者
Liu, Kang [1 ]
Kang, Longyun [1 ]
Wan, Lei [1 ]
Xie, Di [1 ]
Li, Jie [1 ]
机构
[1] South China Univ Technol, Sch Elect Power, Guangzhou 510641, Peoples R China
关键词
Remaining useful life; Lithium -ion battery; Sliding window technique; Box -Cox transformation; Monte Carlo simulation; MODEL; PROGNOSTICS; MANAGEMENT; STATE;
D O I
10.1016/j.est.2023.109352
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Accurate remaining useful life (RUL) prediction technique matters in lithium-ion battery use, optimization, and replacement. This article presents an RUL prediction method combining the sliding window (SW) technique and Box-Cox transformation (BCT). This method achieves online RUL prediction with acceptable accuracy, is independent of offline training data, and only brings a low computational burden. The SW technique is employed for gathering a certain amount of capacity data, which is subsequently transformed using BCT-related techniques to construct a capacity degradation model. The identified model is then extrapolated to predict battery RUL, and the prediction uncertainties are calculated through Monte Carlo (MC) simulation. A simple implementation shows that this hybrid method outperforms the history-based polynomial and BCT methods in battery RUL prediction. Given the segmented capacity degradation trend, a constraint can be imposed on the model parameter for optimization purposes. Experimental results demonstrate that the optimized method obtains lower root-meansquare errors (RMSEs) of RUL predictions during the last 20 % of the battery lifetime than the original one, and the precise RULs are predicted with standard deviations mainly within [1, 10] cycles.
引用
收藏
页数:11
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