Degradation Capacity Spatial-Temporal Embedding RUL Prediction Framework for Lithium-ion Batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is crucial to ensure the safe and reliable operation of the energy storage and power supply systems. However, RUL prediction is significantly affected by the challenges posed by multi-dimensional nonlinearity. Embedding spatial-temporal variables helps reveal nonlinear relationships, making the degradation capacity spatial-temporal embedding an effective approach for extracting patterns and trends of battery degradation within multi-dimensional subspaces. Motivated by this, this work proposes a phase space reconstruction (PSR) approach that utilizes the C-C method combined with a convolutional neural network-bidirectional LSTM and hyperspace attention mechanism (CNN-BiLSTM-HAM) to address this challenge. First, the C-C method performs PSR according to time delay and embedding dimension values to transform degradation capacity data from one-dimensional time series into a multi-dimensional format. Next, the reconstructed capacity matrix is processed by the CNN to generate feature vectors that preserve the spatial structure and location information of the input data, while discarding irrelevant information. These feature vectors serve as input for training the BiLSTM. Finally, the HAM is used to allocate the weights of different feature subsets simultaneously. The proposed CNN-BiLSTM-HAM model and C-C method are validated using the NASA dataset. Experimental results demonstrate that the proposed method yields accurate RUL prediction, with the absolute error, mean absolute error, and root mean square error all being less than 2, 1.3%, and 2%, respectively.