Parallel deep learning with attention-gated fusion for robust battery health monitoring under dynamic operating conditions

Key Points

• Battery health monitoring shows improved accuracy and reliability under fluctuating conditions—addressing key challenges.
• The attention-gated fusion methodology enhances model performance compared to traditional approaches, optimizing battery evaluation.
• Deep learning techniques were used to analyze battery performance metrics in real-time across varying operational scenarios.
• These findings may enable better predictions of battery lifespan, allowing for more effective energy management systems.