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A method for capacity prediction of lithium-ion batteries under small sample conditions

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  • Zhang, Meng
  • Kang, Guoqing
  • Wu, Lifeng
  • Guan, Yong

Abstract

Accurate life prediction of lithium-ion battery is very important for the safe operation of battery system. At present, the data-driven life prediction method is an effective method. However, it is difficult to obtain full life cycle data of long-life lithium batteries, which leads to low accuracy of prediction results. In addition, the degradation of lithium-ion batteries has different trends in different stages, the commonly used methods are insufficient to describe global time variables which make it difficult to adapt to changes in different stages of lithium-ion battery capacity degradation. To solve the above problems, the paper proposes a deep adaptive continuous time-varying cascade network based on extreme learning machines (CTC-ELM) under the condition of small samples. First, a virtual sample generation method based on multi-population differential evolution is proposed, which uses multi-distribution overall trend diffusion technology to adaptively determine the virtual sample range, and combines with the improved differential evolution algorithm to achieve small sample data amplification. Then, a new prediction network with CTC-ELM is constructed. Finally, it is verified on different data sets. Experiments show that the method proposed can effectively expand the sample set of lithium-ion batteries and achieve high accuracy in the estimation of lithium-ion battery capacity.

Suggested Citation

  • Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221023422
    DOI: 10.1016/j.energy.2021.122094
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    References listed on IDEAS

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    Cited by:

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    3. Chuang Sun & An Qu & Jun Zhang & Qiyang Shi & Zhenhong Jia, 2022. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    5. Lyu, Zhiqiang & Wang, Geng & Gao, Renjing, 2022. "Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework," Energy, Elsevier, vol. 251(C).
    6. Hu, Xiaoqian & Wang, Chao & Lim, Ming K. & Chen, Wei-Qiang & Teng, Limin & Wang, Peng & Wang, Heming & Zhang, Chao & Yao, Cuiyou & Ghadimi, Pezhman, 2023. "Critical systemic risk sources in global lithium-ion battery supply networks: Static and dynamic network perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
    8. Wang, Shunli & Wu, Fan & Takyi-Aninakwa, Paul & Fernandez, Carlos & Stroe, Daniel-Ioan & Huang, Qi, 2023. "Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-curren," Energy, Elsevier, vol. 284(C).
    9. Kang, Jihyeon & Atwair, Mohamed & Nam, Inho & Lee, Chul-Jin, 2023. "Experimental and numerical investigation on effects of thickness of NCM622 cathode in Li-ion batteries for high energy and power density," Energy, Elsevier, vol. 263(PE).
    10. Olabi, Abdul Ghani & Abbas, Qaisar & Shinde, Pragati A. & Abdelkareem, Mohammad Ali, 2023. "Rechargeable batteries: Technological advancement, challenges, current and emerging applications," Energy, Elsevier, vol. 266(C).
    11. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    13. Yong Zhu & Mingyi Liu & Lin Wang & Jianxing Wang, 2022. "Potential Failure Prediction of Lithium-ion Battery Energy Storage System by Isolation Density Method," Sustainability, MDPI, vol. 14(12), pages 1-14, June.

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