AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Results demonstrate that coordinate-aware Mamba2 achieves superior accuracy, lower computational cost, faster training and inference, and stronger robustness to different prediction starting points.
The proposed DOA-VMD-CNN-TimeXer framework for RUL-oriented prognosis through capacity trajectory prediction and reconstruction shows closer agreement with the measured degradation trajectories and more stable prediction behavior than the compared methods, indicating its effectiveness for lithium-ion battery RUL-orient...
The results demonstrate that the proposed model significantly outperforms other comparison methods in terms of MAE, MAPE, and RMSE for three battery cells under both 80% and 60% training set ratios, confirming its comprehensive superiority in estimation accuracy, robustness, and generalization capability with limited s...
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for the reliability and safety of modern energy systems. However, the capacity regeneration (CR) phenomenon, a temporary recovery of capacity during cycling or rest, introduces non-monotonic fluctuations in degradation trajecto...
A hybrid prediction framework integrating convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), attention mechanisms, and ensemble learning strategies is developed to improve prediction accuracy and robustness in lithium-ion battery remaining useful life prediction.
Lithium-ion batteries are widely used in transportation and energy storage systems, where accurate remaining useful life (RUL) prediction is essential for safety management and maintenance planning. However, battery degradation is often affected by capacity regeneration, measurement noise, and non-monotonic local fluct...
Lithium-ion batteries have been widely utilized in modern society due to their excellent performance, making the accurate prediction of their remaining useful life (RUL) of paramount importance. Current RUL prediction methods are primarily categorized into battery discharge model-based approaches and data-driven approa...
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