Accurate remaining useful life (RUL) prediction is of great importance to the battery management second-life utilization. This paper proposes a novel hybrid data-driven RUL prediction method based on Gaussian process regression (GPR) and long-short term memory neural network (LSTM). An initial prediction of RUL through LSTM is employed as the mean function of GPR instead of simply assuming it to be zero or a linear form. The aging data of four batteries from NASA data repository is used for model verification and comparison. The results show that the proposed LSTM-GPR approach has higher prediction accuracy than the traditional LSTM and GPR approaches with less training data.


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    Title :

    A Hybrid Approach Based on Gaussian Process Regression and LSTM for Remaining Useful Life Prediction of Lithium-ion Batteries


    Contributors:
    Guo, Xiaoyu (author) / Yang, Zikang (author) / Liu, Yujia (author) / Fang, Zhendu (author) / Wei, Zhongbao (author)


    Publication date :

    2023-06-21


    Size :

    1972218 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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