The prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal processing technology and neural network technology introduces new data-driven methods to RUL prediction and SOH monitoring of the lithium-ion battery. This paper presents a neural-network-based method that combines long short-term memory (LSTM) network with particle swarm optimization and attention mechanism for RUL prediction and SOH monitoring of the lithium-ion battery. Before predicting RUL of the lithium-ion battery, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized for the raw data denoising, which can improve the accuracy of prediction. A real-life cycle dataset of lithium-ion batteries from NASA is used to evaluate the proposed method, and the experiment results show that when compared with traditional methods, the proposed method has higher accuracy. INDEX TERMS Lithium-ion battery, prognostic and health management (PHM), long short-term memory (LSTM), attention mechanism.

Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is introduced. Finally, we apply the attention mechanism to further improve the accuracy of the algorithm. Compared with traditional neural network methods, the method proposed in this paper has higher efficiency and accuracy. INDEX TERMSRUL prediction, variational mode decomposition (VMD), extreme learning machine (ELM), prognostic and health management (PHM), grey wolf optimizer (GWO), differential evolution (DE), attention mechanism.

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In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction.