AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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...
A Whale Optimization Algorithm (WOA)-optimized Convolutional Neural Network (CNN)–Temporal Convolutional Network (TCN)–Temporal Pattern Attention (TPA) framework for lithium-ion battery SOH estimation, achieving higher estimation accuracy and stability.
A convolution-based feature extraction and residual fusion module is introduced ahead of the Mamba backbone to enhance local variation information in the input signals and to facilitate the modelling of long-term temporal dependencies during battery operation.
This study proposes a degradation-aware dynamic masking augmentation method combined with a multiscale CNN–Transformer network to address non-stationary monitoring data, limited informative degradation samples, and the difficulty of jointly modeling local degradation patterns and temporal dependencies.
Accurate State-of-Health (SOH) prediction is essential for deploying retired electric vehicle batteries into reliable second-life energy storage systems. However, this task is challenged by sparse and noisy operational data from onboard Battery Management Systems (BMS). This study systematically evaluates a spectrum of...
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...
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