Prediction of State-of-Health and Remaining-Useful-Life of Battery Based on Hybrid Neural Network Model

Abstract

Battery energy storage systems, especially lithium-ion batteries, have become more common in power systems owing to their numerous advantages, such as supporting voltage and frequency regulation and contributing to peak shaving and load shifting. However, when the battery reaches its end-of-life, it becomes more unstable, leading to a higher probability of system operation failure and safety accidents. Therefore, to accurately predict the State of Health (SOH) and the Remaining Useful Life (RUL) of a battery system, a prediction method is proposed in this paper based on Empirical Mode Decomposition (EMD), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), and Attention Mechanism (AM). Firstly, capacity and different health indicators with high correlation extracted from the battery’s charging and discharging characteristics are considered inputs. Then, the EMD method decomposes the battery data into several intrinsic mode functions (IMFs) and a residual. In the second part, with IMFs and a residual as input parameters, the SOH and RUL of different battery datasets are predicted by using the combined model CNN-BiLSTM-AM. To validate the accuracy of the proposed method, different comparative models are considered and carried out on CALCE and NASA battery degradation datasets. The results illustrate that the errors of the proposed method, which are root mean square error and mean absolute error are at least 48% and 19% more accurate than others in all battery datasets, showing the effectiveness and accuracy of the proposed model in predicting the SOH and RUL of the battery.

Keywords

• State of health
• remaining useful life
• health indicator
• correlation coefficient
• convolutional neural network
• bidirectional long short-term memory