The decoder (Fig. ) takes on the task of generating future state predictions. It is initialized with the hidden and cell states from the encoder and begins by processing the most recent historical cycle data. The model then integrates its own previous predictions and known future conditions, such as the expected discharge current and the cycle number. These two inputs are temporally encoded to capture their positional relevance , ensuring that the decoder is informed of the predefined condition and the timing of each data point within the life cycle. The decoder employs an attention mechanism that can dynamically adjust sequence weights, identifying critical information at each prediction step. This approach overcomes the limitations of static-length vector representation in conventional encoder-