A data-driven approach based on deep neural networks for lithium-ion battery prognostics

Remaining useful life estimation is gaining attention in many real-world applications to alleviate maintenance expenses and increase system reliability and efficiency. Deep learning approaches have recently provided a significant improvement in the estimation of remaining useful life (RUL) and degradation progression concerning machinery prognostics. This research presents a new data-driven approach for RUL estimation using a hybrid deep neural network that combines CNN, LSTM, and classical neural networks. The presented CNN-LSTM neural network aims to extract the spatio-temporal relations in multivariate time series data and capture nonlinear characteristics to achieve better RUL prediction accuracy. To improve the proposed model's performance, PSO is handled to simultaneously optimize the hyperparameters of the network consisting of the number of epochs, the number of convolutional and LSTM layers, the size of units (or filters) in each convolutional, and LSTM layers. Besides, the proposed model in this paper, called the CNN-LSTM-PSO, realizes the multi-step-ahead prediction. In the experimental studies, the popular lithium-ion battery dataset presented by NASA is selected to verify the CNN-LSTM-PSO approach. The experimental consequences revealed that the presented CNN-LSTM-PSO model gives better results than other state-of-the-art machine learning techniques and deep learning approaches considering various performance criteria.

Automated summary

This research introduces a new data-driven approach for estimating remaining useful life by combining CNN, LSTM, and classical neural networks. The presented CNN-LSTM neural network extracts spatio-temporal relations in time series data to achieve better RUL prediction accuracy. By optimizing network hyperparameters with PSO, the model achieves multi-step-ahead prediction.