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This study presents an end-to-end neural network framework for estimating the state-of-health (SOH) and predicting the remaining useful life (RUL) of electric vehicle lithium batteries. The framework employs a hybrid neural network combining one-dimensional convolutional neural networks and active-state-tracking long-short-term memory networks to effectively capture hierarchical features and temporal dependencies in battery degradation data. Experimental results demonstrate that the proposed method achieves

Author

Ryan Rafi

Language

EN