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 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 model was then constructed to predict the low-frequency components, and a temporal convolutional network-attention mechanism-deep neural network 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 2 cycles, which was fewer than those of other comparable approaches. Accordingly, the proposed approach has favorable generalizability and robustness.