A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments

Accurate remaining useful life (RUL) estimation is crucial for the normal and safe operations of lithium-ion batteries (LIBs). Traditionally, every cycle's maximum discharging capacity should be measured and then serve as a model input to predict iteratively the degradation trajectory. Unfortunately, full discharge stages are not always present in practice. Herein, this study presents a hybrid approach consisting of signal decomposition and deep learning to overcome the above limitations. Firstly, for the collected discharging fragments, the convolutional neural networks model predicts every cycle's maximum discharging capacity which combines to form a predicted capacity degradation curve before the start point of RUL prediction. Then, via empirical mode decomposition, this curve's global degradation trend is extracted and serves as the subsequent model input. Finally, the entire degradation trajectory and RUL value could be inferred based on the well-trained gated recurrent unit-fully connected model. The superior prediction performance of the proposed method is verified on two open battery datasets. All the estimation errors can be maintained within 7.0% based on the discharging fragment of the ∼20% capacity ratio ranges from 40% to 60% of the degradation data. This result illustrates the promising accuracy and robustness of the developed LIBs RUL estimation method, especially for not full discharge process in practice.