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A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting.

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Title: A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting.
Authors: Liu, Zhi-Feng1 (AUTHOR) liuzhifeng@tust.edu.cn, Huang, Ya-He1 (AUTHOR) huangyahe@mail.tust.edu.cn, Zhang, Shu-Rui1 (AUTHOR) zsr030923@mail.tust.edu.cn, Luo, Xing-Fu1 (AUTHOR) fu406699@mail.tust.edu.cn, Chen, Xiao-Rui1 (AUTHOR) 21021127@mail.tust.edu.cn, Lin, Jun-Jie1 (AUTHOR), Tang, Yu2,3 (AUTHOR), Guo, Liang4 (AUTHOR) guoliang@sgepri.sgcc.com.cn, Li, Ji-Xiang1 (AUTHOR) lijixiangtust@126.com
Source: Applied Energy. Jan2025:Part D, Vol. 377, pN.PAG-N.PAG. 1p.
Subjects: Remaining useful life, Probability density function, Deep learning, Short-term memory, Lithium-ion batteries
Abstract: Lithium-ion batteries (LIBs) are widely employed in electric vehicles due to their environmental friendliness and extended lifespan. However, accurately forecasting the remaining useful life of LIBs presents challenges owing to intricate internal electrochemical reactions and external environmental uncertainties. Therefore, this study proposes a new LSTM-Informer deep learning model based on cooperative interaction gates for lithium-ion battery capacity point-interval prediction. Specifically, building upon the three gating mechanisms of LSTM, a novel cooperative interaction gate is proposed to thoroughly explore the direct correlation between the degraded sequences; and a long and short-term memory weight control strategy based on the capacity regeneration ratio is introduced to dynamically adjust the influence of the four gating mechanisms based on the fluctuation degree of the data, so as to accurately capture the capacity regeneration phenomenon of the degraded sequences, and to increase the prediction point prediction precision. The efficient ProbSparse is introduced to improve the prediction performance at the later stage of sequence data. Based on the interval prediction Kernel Density Estimation model, a dynamic optimal bandwidth optimization strategy is constructed to adaptively adjust the interval width according to the data fluctuation characteristics, which improves the uncertainty expression of the model. The results demonstrate that the proposed model achieves impressive prediction performance for different types of batteries, with the point prediction evaluation index MAPE controlled below 1.5 % and the interval prediction evaluation index CWC controlled below 0.7. [Display omitted] • Introducing a novel collaborative interaction gate-based deep learning model. • Proposing an innovative capacity regeneration ratio-based long short-term memory weight control strategy. • Presenting the Informer with effective ProbSparse as a unique advancement to reduce computational complexity. • Devising an innovative dynamic optimal bandwidth adjustment strategy-based LIB capacity interval forecasting method. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Lithium-ion batteries (LIBs) are widely employed in electric vehicles due to their environmental friendliness and extended lifespan. However, accurately forecasting the remaining useful life of LIBs presents challenges owing to intricate internal electrochemical reactions and external environmental uncertainties. Therefore, this study proposes a new LSTM-Informer deep learning model based on cooperative interaction gates for lithium-ion battery capacity point-interval prediction. Specifically, building upon the three gating mechanisms of LSTM, a novel cooperative interaction gate is proposed to thoroughly explore the direct correlation between the degraded sequences; and a long and short-term memory weight control strategy based on the capacity regeneration ratio is introduced to dynamically adjust the influence of the four gating mechanisms based on the fluctuation degree of the data, so as to accurately capture the capacity regeneration phenomenon of the degraded sequences, and to increase the prediction point prediction precision. The efficient ProbSparse is introduced to improve the prediction performance at the later stage of sequence data. Based on the interval prediction Kernel Density Estimation model, a dynamic optimal bandwidth optimization strategy is constructed to adaptively adjust the interval width according to the data fluctuation characteristics, which improves the uncertainty expression of the model. The results demonstrate that the proposed model achieves impressive prediction performance for different types of batteries, with the point prediction evaluation index MAPE controlled below 1.5 % and the interval prediction evaluation index CWC controlled below 0.7. [Display omitted] • Introducing a novel collaborative interaction gate-based deep learning model. • Proposing an innovative capacity regeneration ratio-based long short-term memory weight control strategy. • Presenting the Informer with effective ProbSparse as a unique advancement to reduce computational complexity. • Devising an innovative dynamic optimal bandwidth adjustment strategy-based LIB capacity interval forecasting method. [ABSTRACT FROM AUTHOR]
ISSN:03062619
DOI:10.1016/j.apenergy.2024.124741