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A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions

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Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12487))

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Abstract

The monitoring information of the Lithium-ion batteries is influenced by variable operation conditions. Many health assessment approaches acquire the battery monitoring information to assess the battery health status. However, these approaches have poor adaptability under variable operation conditions. This paper presents a new health assessment approach of the Lithium-ion batteries under variable operation conditions. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adapts a multiple dimensionality reduction approach based on the locally linear embedding and Isomap. Moreover, the synthetical correlation coefficient is proposed to evaluate the ability of the method to be immune to variable operation conditions. Finally, the example illustrates the effectiveness of the proposed method.

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Acknowledgments

The authors are highly thankful for National Key Research Program (2019YFB1706001), National Natural Science Foundation of China (61773001), Industrial Internet Innovation Development Project (TC190H46B).

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Authors and Affiliations

  1. Beihang University, Beijing, China

    Sheng Hong

  2. Nanchang University, Nanchang, Jiangxi, China

    Yining Zeng

Authors
  1. Sheng Hong
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  2. Yining Zeng
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Corresponding author

Correspondence to Sheng Hong .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

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Hong, S., Zeng, Y. (2020). A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_17

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  • DOI: https://doi.org/10.1007/978-3-030-62460-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62459-0

  • Online ISBN: 978-3-030-62460-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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