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Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning.

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Title: Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning.
Authors: Hong, Joonki1 (AUTHOR), Lee, Dongheon1 (AUTHOR), Jeong, Eui-Rim1,2 (AUTHOR), Yi, Yung1 (AUTHOR) yiyung@kaist.edu
Source: Applied Energy. Nov2020, Vol. 278, pN.PAG-N.PAG. 1p.
Subjects: Forecasting, Convolutional neural networks, Lithium-ion batteries, Electric vehicle batteries, Error rates
Abstract: This paper presents the first full end-to-end deep learning framework for the swift prediction of lithium-ion battery remaining useful life. While lithium-ion batteries offer advantages of high efficiency and low cost, their instability and varying lifetimes remain challenges. To prevent the sudden failure of lithium-ion batteries, researchers have worked to develop ways of predicting the remaining useful life of lithium-ion batteries, especially using data-driven approaches. In this study, we sought a higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature. We took an in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate. • The first full end-to-end deep learning framework for battery RUL prediction. • Swift RUL prediction with only four cycles of the target battery (25X faster). • Analyzing temporal patterns of terminal voltage, current, and cell temperature. • Reliable use of deep learning-based RUL prediction via uncertainty estimates. • Interpretable analysis on RUL prediction of deep neural networks. [ABSTRACT FROM AUTHOR]
Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 147202899
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: This paper presents the first full end-to-end deep learning framework for the swift prediction of lithium-ion battery remaining useful life. While lithium-ion batteries offer advantages of high efficiency and low cost, their instability and varying lifetimes remain challenges. To prevent the sudden failure of lithium-ion batteries, researchers have worked to develop ways of predicting the remaining useful life of lithium-ion batteries, especially using data-driven approaches. In this study, we sought a higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature. We took an in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate. • The first full end-to-end deep learning framework for battery RUL prediction. • Swift RUL prediction with only four cycles of the target battery (25X faster). • Analyzing temporal patterns of terminal voltage, current, and cell temperature. • Reliable use of deep learning-based RUL prediction via uncertainty estimates. • Interpretable analysis on RUL prediction of deep neural networks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=147202899
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      – Type: doi
        Value: 10.1016/j.apenergy.2020.115646
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      – Code: eng
        Text: English
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        PageCount: 1
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      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Lithium-ion batteries
        Type: general
      – SubjectFull: Electric vehicle batteries
        Type: general
      – SubjectFull: Error rates
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      – TitleFull: Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning.
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            NameFull: Lee, Dongheon
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            NameFull: Jeong, Eui-Rim
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              Text: Nov2020
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              Y: 2020
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