This mechanism expands the model's capacity to focus on different positions of the input sequence simultaneously , which is crucial for capturing a wider range of dependencies inherent in battery lifetime data. This attention mechanism operates by projecting the decoder's hidden states and the encoder outputs, representing the past cycle's information, into multiple subspaces. This is formulated as: (Eq. 4)