This technique allows for the integration of the last observed data or transition to the decoder's selfgenerated predictions, supplemented with temporally encoded future conditions. During training, a dynamic teacher forcing strategy is employed, in which actual target outputs are used as inputs in lieu of previous predictions to promote model convergence, prediction fidelity, and generalizability in the model. This hybrid training strategy allows effective learning from the ground truth while gradually becoming equipped for self-guided predictions. At the end of the processing of this sequence, quantile-based predictions are collected into a stack of tensors, encapsulating a comprehensive forecast for subsequent decision-making processes. Thus, this forward pass provides fine-grained, probabilistic understanding of the evolving battery life-cycle stages, with the potential to inform risk assessment and optimize operational efficiency.