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Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine

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  • Cai, Yishan
  • Yang, Lin
  • Deng, Zhongwei
  • Zhao, Xiaowei
  • Deng, Hao

Abstract

The state-of-health (SOH) of a lithium-ion battery is a key parameter in battery management systems. However, current approaches to estimating the SOH of a lithium-ion battery are mainly offline or have not solved the accuracy and efficiency problems. This paper attempts to solve these problems. A dynamic information extraction method based on a fast discrete wavelet transform is proposed to greatly improve the algorithm efficiency. Dimension reduction is performed on the battery current and voltage time series using the maximum entropy partition method to individually generate a symbolic time series. A cross D-Markov machine model is built based on the causal symbolic time series to extract the feature parameter and represent the lithium-ion battery SOH. An accelerated aging experiment using LiFePO4 batteries is conducted to identify different aging stages. The results show that the feature parameter is an accurate representation of the lithium-ion battery SOH, the maximum error of SOH can be within 0.113, and the average error can be within 0.0509 in the entire battery life cycle. The proposed method is more suitable for online application than the previous method because its computation time is 250–290 times shorter.

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  • Cai, Yishan & Yang, Lin & Deng, Zhongwei & Zhao, Xiaowei & Deng, Hao, 2018. "Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine," Energy, Elsevier, vol. 147(C), pages 621-635.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:621-635
    DOI: 10.1016/j.energy.2018.01.001
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    References listed on IDEAS

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    4. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
    5. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    6. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    7. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    8. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    9. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    10. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
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    12. Li, Dongdong & Yang, Lin & Li, Chun, 2021. "Control-oriented thermal-electrochemical modeling and validation of large size prismatic lithium battery for commercial applications," Energy, Elsevier, vol. 214(C).

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