State-of-health and remaining-useful-life estimations of lithium-ion battery based on temporal convolutional network-long short-term memory

Accurate estimations in state of health (SOH) and remaining useful life (RUL) are significant for safe and efficient operation of batteries. With the development of big data and deep learning technology, the neural network method has been widely used for SOH and RUL estimations because of its excellent nonlinear mapping performance, adaptive performance and self-learning performance. In this paper, a novel hybrid model based on temporal convolutional network-long short-term memory (TCN-LSTM) for SOH and RUL estimations is proposed. The hyperparameters of each layer in the model are optimized using Bayesian optimization algorithm. Three different models, including convolutional neural network-long short-term memory (CNN-LSTM) model, temporal convolutional network (TCN) model and long short-term memory (LSTM) model, are adopted as comparisons to evaluate the performance of the proposed model. All the models are tested using two public battery datasets from National Aeronautics and Space Administration (NASA dataset) and Oxford University (OX dataset). In SOH task, the TCN-LSTM model achieves an accuracy improvement of >16 % and 14 % in NASA and OX datasets, respectively. In RUL task, the accuracies of the TCN-LSTM model and the CNN-LSTM model are superior to other models in NASA dataset; while the LSTM model and the CNN-LSTM model have better performance in OX dataset.