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What is Hybrid Deep Learning for Battery RUL Prediction about?

This study presents a hybrid deep learning approach for predicting the remaining useful life (RUL) of lithium-ion batteries using discharging fragments. The method combines convolutional neural networks (CNN) for maximum discharging capacity prediction, empirical mode decomposition (EMD) for extracting global degradation trends, and a gated recurrent unit (GRU) model for RUL estimation. The proposed approach demonstrates high accuracy, maintaining estimation errors within 7.0% across various battery dataset

Author

Marsha Clarabell

Language

EN