The instability of lithium-ion batteries may result in system operation failure and cause safety accidents, thus predicting the remaining useful life (RUL) accurately is helpful for reducing the risk of battery failure and extending its useful life. In this paper, a hybrid model based on TCN-GRU-DNN and dual attention mechanism is proposed for enhancing the RUL prediction accuracy of lithium-ion batteries. Firstly, the Temporal Convolutional Network (TCN) with a feature attention mechanism is applied to form an encoder module to capture the battery capacity regeneration phenomenon, and then a Gated Recurrent Unit (GRU) with a temporal attention mechanism is denoted as a decoder module for better characterizing the decay trend of the capacity series. Finally, the final prediction results are output through a Deep Neural Network (DNN). We conducted experiments on two data sets NASA and CALCE. The prediction errors are presented in subsequent experiments under different evaluation standards such as absolute error (AE), root mean square error (RMSE), mean absolute error (MAE), and R-squared error (R²). The experimental results demonstrate that the proposed model can achieve a more accurate prediction for RUL on lithium-ion batteries, with RMSE does not exceed 2.407% in the NASA dataset and does not exceed 0.897% in the CALCE lithium-ion battery dataset respectively.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries enables their timely replacement and ensures the proper operation of equipment. This study presents a novel hybrid approach for predicting nonlinear and nonsmooth battery capacity sequences. To develop this approach, first, the original battery capacity sequence was adaptively decomposed through Northern Goshawk Optimization (NGO)-Variational Mode Decomposition (VMD). NGO-VMD could efficiently extract useful information at different scales and could considerably reduce the complexity of the battery capacity sequence. Second, the decomposed sequences were grouped into high- and low-frequency components on the basis of the over-zero rate. A convolutional neural network-bidirectional long short-term memory model was then constructed to predict the low-frequency components, and a temporal convolutional network-attention mechanism-deep neural network model was developed to predict the high-frequency components. In addition, a tensor-based transfer learning approach was employed to predict the low-frequency components of capacity sequences from same-type batteries. The RUL prediction errors of the proposed approach did not exceed 2 cycles, which was fewer than those of other comparable approaches. Accordingly, the proposed approach has favorable generalizability and robustness.

With the rapid development of artificial intelligence, deep learning has become widely used to monitor the state of permanent magnet synchronous motors (PMSMs). However, to achieve accurate condition detection, datasets with large quantities of labeled data must be used for model training. Accuracy decreases considerably when the quantity of labeled data is insufficient. To address this problem, a fault diagnosis method based on an improved deep Q-network (DQN) is designed in this study to detect inter-turn short circuit (ITSC) faults. In this method, first, a one-dimensional group convolutional neural network–based conditional generative adversarial network is used to expand the collected dataset. Subsequently, a DQN-based fault diagnosis method is applied. A one-dimensional deep residual shrinkage network and the prioritized experience replay strategy are introduced into the original network structure, and the sampling strategy of the original network is optimized. Finally, the fault diagnosis capability of the proposed method was evaluated in several experiments. The proposed method considerably outperformed competing algorithms: it had a diagnosis rate of 98.49% for the ITSC faults of PMSMs and a diagnosis rate of 99.50% on the bearing dataset of Case Western University with certain generalizations. The proposed method is promising for use in circuit fault detection.

Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial for enhancing the safety, reliability, and efficiency of battery-powered applications like electric vehicles and energy storage systems. This review analyzed over 200 peer-reviewed studies and categorized RUL prediction methods into three major approaches: physics-based, data-driven, and hybrid models. Hybrid models, which combine physical insights with data-driven methods, are the most widely used due to their adaptability, accuracy, and interpretability. Data-driven models, such as long short-term memory and convolutional neural networks, excel in capturing complex, nonlinear relationships but require large datasets and high computational power. While physics-based models offer high accuracy, they are less commonly employed due to their complexity and extensive parameter tuning requirements. Despite their benefits, hybrid models face challenges, including increased computational complexity and integration difficulties. This review also highlights key datasets and evaluation metrics used in LIB RUL prediction. The NASA dataset is the most frequently used, appearing in 30.8% of the papers, followed by the CALCE dataset. Root mean square error is the most common evaluation metric, used in 29.6% of the studies, followed by mean absolute error and mean absolute percentage error, which are essential for assessing prediction accuracy. Through comparative analysis, this review identified key challenges and outlined future research directions, including the need for lightweight hybrid models, standardized benchmarking datasets, and uncertainty-aware evaluation frameworks to support real-time, robust battery management systems. In conclusion, the future of LIB RUL prediction lies in the integration of advanced hybrid models, improved datasets, and uncertainty-aware performance metrics, with a focus on refining data-driven approaches for handling real-time, multi-sensor data.