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A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery
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Liu, Kailong, Shang, Yunlong, Ouyang, Quan and Widanage, Widanalage Dhammika (2021) A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Transactions on Industrial Electronics, 68 (4). pp. 3170-3180. doi:10.1109/TIE.2020.2973876 ISSN 0278-0046.
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WRAP-A-data-driven-approach-uncertainty-lithium-ion-battery-Liu-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1109/TIE.2020.2973876
Abstract
Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This paper applies advanced machine-learning techniques to achieve effective future capacities and RUL prediction for lithium-ion batteries with reliable uncertainty management. To be specific, after using the empirical mode decomposition (EMD) method, the original battery capacity data is decomposed into some intrinsic mode functions (IMFs) and a residual. Then the long short term memory (LSTM) sub-model is applied to estimate the residual while the gaussian process regression (GPR) sub-model is utilized to fit the IMFs with the uncertainty level. Consequently, both the long-term dependence of capacity and uncertainty quantification caused by the capacity regenerations can be captured directly and simultaneously. Experimental aging data from different batteries are deployed to evaluate the performance of proposed LSTM+GPR model in comparison with the solo GPR, solo LSTM, GPR+EMD and LSTM+EMD models. Illustrative results demonstrate the combined LSTM+GPR model outperforms other counterparts and is capable of achieving accurate results for both 1-step and multi-step ahead capacity predictions. Even predicting the RUL at the early battery cycle stage, the proposed data-driven approach still presents good adaptability and reliable uncertainty quantification for battery health diagnosis.
Item Type: | Journal Article | ||||||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
Library of Congress Subject Headings (LCSH): | Electric vehicles , Electric vehicles -- Batteries , Lithium ion batteries, Lithium ion batteries -- Deterioration | ||||||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Electronics | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISSN: | 0278-0046 | ||||||||||||
Official Date: | April 2021 | ||||||||||||
Dates: |
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Volume: | 68 | ||||||||||||
Number: | 4 | ||||||||||||
Page Range: | pp. 3170-3180 | ||||||||||||
DOI: | 10.1109/TIE.2020.2973876 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Re-use Statement: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 26 June 2020 | ||||||||||||
Date of first compliant Open Access: | 26 June 2020 | ||||||||||||
RIOXX Funder/Project Grant: |
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