A physics-informed neural network (PINN) was also studied based on its capacity to treat both physical information as well as synthetic data on the ML performance and the SoH estimation abilities, by Hofmann et al. . In this paper, they concluded that a combined dataset was the best for the PINN training, since the inclusion of internal states in the simulation dataset increases the performance.