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Remaining useful life prediction for lithium-ion battery using a data-driven method

Remaining useful life prediction for lithium-ion battery using a data-driven method Accurate prediction of the remaining useful life (RUL) of Li-ion batteries is one of the key technologies in the Battery Management System (BMS). To boost the prediction accuracy of Li-ion battery RUL, a data-driven approach is developed, through the combination of Long and Short-Term Memory (LSTM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). First and foremost, the battery capacity extracted from the National Aeronautics and Space Administration (NASA) battery data set is used as original data and the CEEMDAN is utilised to deal with original data into components of dissimilar frequencies. Then, the LSTM model is used to predict components of different frequencies. Finally, the CEEMDAN-LSTM prediction result is efficaciously integrated to acquire the final prediction of the Li-ion battery RUL. The results show that the proposed method is superior for Li-ion battery RUL prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Wireless and Mobile Computing Inderscience Publishers

Remaining useful life prediction for lithium-ion battery using a data-driven method

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1741-1084
eISSN
1741-1092
DOI
10.1504/ijwmc.2022.127586
Publisher site
See Article on Publisher Site

Abstract

Accurate prediction of the remaining useful life (RUL) of Li-ion batteries is one of the key technologies in the Battery Management System (BMS). To boost the prediction accuracy of Li-ion battery RUL, a data-driven approach is developed, through the combination of Long and Short-Term Memory (LSTM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). First and foremost, the battery capacity extracted from the National Aeronautics and Space Administration (NASA) battery data set is used as original data and the CEEMDAN is utilised to deal with original data into components of dissimilar frequencies. Then, the LSTM model is used to predict components of different frequencies. Finally, the CEEMDAN-LSTM prediction result is efficaciously integrated to acquire the final prediction of the Li-ion battery RUL. The results show that the proposed method is superior for Li-ion battery RUL prediction.

Journal

International Journal of Wireless and Mobile Computing – Inderscience Publishers

Published: Jan 1, 2022

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