State-of-health estimation and remaining useful life for lithium-ion battery based on deep learning with Bayesian hyperparameter optimization

Lithium-ion battery state-of-health (SOH) estimation and remaining usable life (RUL) prediction are important for battery prognosis and health management. In this article, a framework-combined deep convolution neural network (DCNN) with double-layer long short-term memory (LSTM) is proposed, which is designed for online health prognosis. Based on the raw data obtained during the constant current charging process, the aging characteristics of the battery can be extracted by DCNN to estimate SOH. The estimation results are then sent to the LSTM for the temporal prediction of the RUL. This framework considers both temporal and spatial characteristics of data, and the powerful spatial feature extraction ability of DCNN and the effectiveness of LSTM for time series problems can ensure the high precision of calculation. At the same time, the hyperparameters of the neural networks, which can highly affect the performance of networks, are obtained by Bayesian optimization to ensure the networks run in the best status. The results show that the proposed method can achieve low root mean square error, which are inferior to 0.0061 and 0.0627 for SOH estimation and RUL prediction, respectively.

Automated summary

A deep learning framework combining DCNN and LSTM is proposed for lithium-ion battery state-of-health estimation and remaining usable life prediction, considering both temporal and spatial characteristics of data and optimizing the neural networks' hyperparameters through Bayesian optimization to ensure high precision calculations. The method achieves low root mean square errors for SOH estimation and RUL prediction.