Another advantage of using DL algorithms is their ability to use raw data, which has gained interest in the estimation of battery State of Health (SOH). For instance, Yang et al. developed a novel hybrid convolutional neural network architecture with parallel residual connections, which utilizes raw data across multiple dimensions. By incorporating attention mechanisms, their model achieves remarkable accuracy in predicting the early stages of degradation. Although these approaches are applied in battery research , their prominence is not as widespread as in other scientific fields. However, this lesser emphasis provides an opportunity for further exploration and discovery. Beyond these early lifetime prediction models, sequence-to-sequence (Seq-to-Seq) models have been used to monitor battery lifetime and SOH . They leverage intrinsic temporal dependencies in degradation data, providing high predictive accuracy and computational efficiency. Li et al. developed a one-shot LSTM-based Seq-to-Seq framework which not only predicts future capacities, but also identifies knee points in the degradation curve, maintaining stability even in the face of stochastic disturbances. Although Seq-to-Seq models demonstrate robust predictions, they also exhibit limitations in generalization and require large and diverse datasets to enhance performance .