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A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis

Author

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  • Bowen Jia

    (College of Information Engineering, Capital Normal University, Beijing 100048, China
    Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China)

  • Yong Guan

    (College of Information Engineering, Capital Normal University, Beijing 100048, China
    Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China)

  • Lifeng Wu

    (College of Information Engineering, Capital Normal University, Beijing 100048, China
    Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China)

Abstract

As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data sets. Firstly, according to importance sampling (IS), valid features are extracted from cycles of charging voltage in both the source and target battery. Secondly, transfer component analysis (TCA) is used to map the source data set to the target data set. Moreover, an extreme learning machine (ELM) algorithm is employed to train a single hidden layer feed forward neural network (SLFN) for its fast training speed and facile to set up. Finally, validation experiments and the comparisons on the results are conducted. The results showed that the proposed framework has a good capability of predicting the SOH of lithium batteries.

Suggested Citation

  • Bowen Jia & Yong Guan & Lifeng Wu, 2019. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis," Energies, MDPI, vol. 12(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2524-:d:244589
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    References listed on IDEAS

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    1. Mingant, R. & Bernard, J. & Sauvant-Moynot, V., 2016. "Novel state-of-health diagnostic method for Li-ion battery in service," Applied Energy, Elsevier, vol. 183(C), pages 390-398.
    2. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
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    Cited by:

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    3. Bing Long & Xiangnan Li & Xiaoyu Gao & Zhen Liu, 2019. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model," Energies, MDPI, vol. 12(17), pages 1-13, August.
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    5. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    6. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).

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