The concept of virtual time in battery aging models addresses the inverse problem [18] of determining the true aging effects under typical usage conditions from the observed effects under accelerated testing conditions. Virtual time offers a theoretical framework that translates the rapid aging observed in accelerated tests into an equivalent aging duration under normal conditions. During accelerated tests, batteries are subjected to higher temperatures and elevated states of charge (SOC) to expedite the aging process and quickly reach the end of life. Although practical for rapid data collection, these conditions do not accurately represent typical battery usage. Therefore, the challenge is to map this accelerated aging data to normal usage scenarios, ensuring that the predictions remain relevant and accurate. virtual time serves as this crucial mapper by enabling the use of models trained in the static laboratory conditions into dynamic normal environments. Thus, virtual time ensures that the accelerated dataset can be reliably mapped to the normal dataset, providing meaningful insights into the battery's aging behavior under real-world conditions. We employed a sequential neural network model to train a virtual time predictor, as can be seen in Figure , based on observed capacity and relevant features. The model architecture includes an input layer compatible with the shape of the training data, followed by two Long Short-Term Memory (LSTM) layers , each with 64 units, ReLU activation, He normal initialization, and no bias. These layers are designed to capture temporal dependencies in the data. Finally, a dense layer with a single neuron and zero initialization (also without bias) produces the virtual time prediction. This setup effectively leverages temporal patterns in the input features to predict virtual time accurately.