In this section, we first evaluate the performance of the fitted models. Knowing that the batteries will rarely be stressed continuously under laboratory static conditions, we utilized all static conditions' test points in the training process without dividing them into separate training, validation, and test sets. This approach ensures that the models are well-calibrated and robust, providing a solid baseline for model accuracy. Subsequently, we validate the performance of the trained models using dynamic conditions' test points. Initially, we validate a dynamic condition test point with the calendar aging model. Following this, we demonstrate the performance of the trained models under two dynamic conditions that encompass both calendar and cyclic aging. Finally, we illustrate the effectiveness of our approach through its application in three novel scenarios.