A novel lithium-ion battery state-of-health estimation method for fast-charging scenarios based on an improved multi-feature extraction and bagging temporal attention network

Computer Science

• Attention (Machine Learning) 100%
• Feature Extraction 100%
• Estimation Method 100%
• Long Short-Term Memory Network 66%
• Generalizability 66%
• Neural Network 33%
• Convolutional Layer 33%
• Evaluation Metric 33%
• Temporal Dimension 33%
• Network Design 33%
• Time Difference 33%
• Data Handling 33%
• Operational Condition 33%

Engineering

• Feature Extraction 100%
• State of Health 100%
• Lithium-Ion Batteries 100%
• Long Short-Term Memory 66%
• Battery (Electrochemical Energy Engineering) 66%
• Metrics 33%
• Comparative Experiment 33%
• Battery System 33%
• Convolutional Layer 33%
• Temporal Dimension 33%
• Operational Condition 33%
• Noise Scenario 33%

Chemical Engineering

• Long Short-Term Memory 100%
• Neural Network 50%