An end-cloud collaboration approach for state-of-health estimation of lithium-ion batteries based on bi-LSTM with collaboration of multi-feature and attention mechanism

This study develops an end-cloud collaboration method for estimating the State-of-Health (SOH) of batteries. It fuses a cloud-based deep learning model for detailed analysis and an end-side model for swift evaluation, employing Bidirectional Long Short Term Memory networks and an attention mechanism for precise feature identification. A comprehensive feature extraction methodology, incorporating incremental capacity and differential thermal analyses, ensures robust correlation with battery degradation. The Extended Kalman Filter integrates these models, providing accurate and timely SOH estimations. Tested against NASA's dataset, the method achieved SOH estimation with errors around 1%, suggesting potential for real-time battery health monitoring and broader multi-state estimation applications.