Posteriorly, Tu et. al demonstrated the use of different hybrid feedforward neural networks (FNNs) . Two different types of hybrid models were studied, and shown to have as high voltage prediction accuracy throughout the LIB cycle at different C-rates. It was found possible to combine these models along with bulk thermal models to predict the electrochemical and thermal behaviors simultaneously. After that, an extra awareness of the aging condition was also embedded on top, by making the ML informed of the battery's SoH. The proposed frameworks on this work while combining several different models, is able to generalize and predict precisely even beyond training datasets. The rationale behind this work was to make the neural network informed of the battery's internal state in order to have results that are physically sound.