Based on Pearson correlation results (Supplementary Figure ), four features with the highest correlation coefficients were selected for input into our ML algorithm: velocity standard deviation, total distance traveled, time to mean velocity, and time to half velocity. Next, seven machine learning regression models were evaluated: linear regression, k-nearest neighbors (KNN), decision tree, random forest, Gaussian process, extremely randomized trees, and neural networks. The training process included data splitting, scaling, hyperparameter tuning, and 10-fold cross-validation to optimize model performance. Model performance was assessed on the testing dataset (12 drive cycles) using the mean absolute percentage error (MAPE), providing a reliable measure of prediction accuracy.