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    Remaining Useful Life Prediction of Lithium Battery Based on Sequential CNN–LSTM Method 

    Source: Journal of Electrochemical Energy Conversion and Storage:;2021:;volume( 018 ):;issue: 004:;page 041005-1
    Author(s): Li, Dongdong; Yang, Lin
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Among various methods for remaining useful life (RUL) prediction of lithium batteries, the data-driven approach shows the most attractive character for non-linear relation learning and accurate prediction. However, the ...
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