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Media type:
E-Article
Title:
Lithium Battery Life Prediction Based on DBN and LSTM
Contributor:
Ran, Bo;
Yuan, Huimei
Published:
IOP Publishing, 2023
Published in:
Journal of Physics: Conference Series, 2433 (2023) 1, Seite 012020
Language:
Not determined
DOI:
10.1088/1742-6596/2433/1/012020
ISSN:
1742-6588;
1742-6596
Origination:
Footnote:
Description:
Abstract Aiming at the low adaptability and inaccuracy of a single network in predicting the Remaining Useful Life (RUL) of lithium batteries, this paper proposed a prediction method based on the combination of Deep Belief Network (DBN) and Mogrifier Long Short-Term Memory network (LSTM). Firstly, the Ensemble Empirical Mode Decomposition (EEMD) method was used to preprocess the capacity data of lithium battery, and the feature information of the data was extracted by correlation analysis, which can effectively solve the regeneration of battery capacity. In addition, the decomposed high-frequency and low-frequency data are trained and predicted by DBN and Mogrifier LSTM networks respectively. The experimental results show that this method has high effectiveness and superiority.