More recently, another study focused on tracking the SoH of batteries under fast cycling conditions was published by Weddle et al. . In this work, a synthetic dataset coming from P2D PBMs was used to create full cycling profiles. The profiles were posteriorly used to train a deep learning algorithm to identify the aging modes. Herein, a CNN was used to identify the features of the cycling as to map the aging modes. At the end, this methodology allowed to build a robust and reliable model to enable a rapid, and continuous aging assessment under different charging conditions.