Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200041-8.doi: 10.11896/jsjkx.241200041

• Big Data & Data Science • Previous Articles     Next Articles

Remaining Useful Life Prediction of Lithium-ion Batteries Based on Frequency-channelAttention Mechanism and MSCNet

LU Shiyu1, WANG Hairui1, ZHU Guifu2,3, LI Yalong1   

  1. 1 School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China
    2 Information Construction Management Center,Kunming University of Science and Technology,Kunming 650504,China
    3 AI Joint Research Center,Kunming University of Science and Technology-Shuguang Information Industry Co.,Ltd.,Kunming 650504,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:LU Shiyu,born in 2000,postgraduate.His main research interests include machine learning and life expectancy prediction.
    ZHU Guifu,born in 1984,master supervisor,senior engineer.His main research interests include intelligent diagnosis technology and education big data.
  • Supported by:
    National Natural Science Foundation of China(61863016).

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Abstract: To address issues such as inaccurate feature extraction,significant data noise,and low precision in tracking capacity degradation trends in lithium-ion battery capacity estimation,a novel model combining the Frequency Channel Attention Mechanism(FCA) and Multi-Scale Inter-Series Correlations Net(MSCNet) is proposed.The model is designed in three stages.Firsty,raw sensor data are preprocessed to remove noise.Secondly,prominent periodic patterns are extracted through frequency domain analysis using the frequency-enhanced attention mechanism.Finally,the multi-scale outputs are aggregated by MSCNet,reducing model parameters while improving effectiveness of feature extraction.Experiments based on publicly available CALCE and NASA datasets demonstrate that the proposed model reduces relative error(RE) in battery life prediction by 10%~20% compared to existing algorithms,enabling more accurate tracking of battery degradation trends.

Key words: Lithiumion battery, Remaining useful life prediction, Attention mechanism, Multi-scale, Inter-series correlation

CLC Number: 

  • TM912
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