Hybrid Data-Driven Approach for Predicting the Remaining Useful Life of Lithium-Ion Batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) enables their timely replacement and ensures the proper operation of equipment. This study presents a novel hybrid approach for predicting nonlinear and nonsmooth battery capacity sequences. To develop this approach, first, the original battery capacity sequence was adaptively decomposed through northern goshawk optimization (NGO)-variational mode decomposition (VMD). NGO-VMD could efficiently extract useful information at different scales and could considerably reduce the complexity of the battery capacity sequence. Second, the decomposed sequences were grouped into high- and low-frequency components on the basis of the over-zero rate. A convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) model was then constructed to predict the low-frequency components, and a temporal convolutional network-attention mechanism-deep neural network (TCN-Attention-DNN) model was developed to predict the high-frequency components. In addition, a tensor-based transfer learning approach was employed to predict the low-frequency components of capacity sequences from same-type batteries. The RUL prediction errors of the proposed approach did not exceed two cycles, which was fewer than those of other comparable approaches. Accordingly, the proposed approach has favorable generalizability and robustness.