fig3

A hybrid deep learning model for robust and data-efficient lithium-ion battery remaining useful life prediction

Figure 3. The architecture of our battery RUL prediction framework. RUL: Remaining useful life; NASA: National Aeronautics and Space Administration; NCM: nickel cobalt manganese oxide; HIs: health indicators; HI: health indicator; IMFs: intrinsic mode functions; IMF: intrinsic mode function; CEEMD: complementary ensemble empirical mode decomposition; CNN: convolutional neural network; BiGRU: bidirectional gated recurrent unit; GRU: gated recurrent unit; BP: back propagation; LSTM: long short-term memory; PAW: present article’s work; MAE: mean absolute error; RMSE: root mean square error; R2: coefficient of determination; MAPE: mean absolute percentage error.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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