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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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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