These route profiles consist of discharge current data corresponding to the velocity and terrain profiles. To obtain voltage-current data, the route profiles were tested via battery cycler using two charging methods which also incorporates human behavior regarding different charging habits. The test and reference route profiles were tested on single random charging method unlike model development data. Therefore, the number of current profiles for testing and reference are same as the output from EV Simulink model. The resulting dataset is comprised of 98 current profiles for modeling purposes, 12 and 9 current profiles for testing and referencing, respectively. The distribution of velocity profiles and current profiles data is shown in Figure to d. Figure illustrates the percentage distribution of velocity ranges for each minitrips for modeling and testing dataset. The largest proportion of time is in the velocity range of 20-25 m/s during each mini-trip, followed by velocities above 25 m/s. Figure shows the discharge capacity drop values for 108 current profiles (modeling and testing) for up to 800 seconds of trip time. For reference, the current profile corresponding to the constant maximum velocity with flat terrain routes also shown in pink. This particular profile is chosen as a reference in later sections as well. Since it maintains a constant maximum velocity during the trip, the resulting discharge capacity drop is comparatively higher than others. Essentially, the discharge capacity is directly proportional to the SOC of the battery. However, the discharge capacity drop, or SOC drop value, is relatively small for trips around 800 seconds. Therefore, we introduce a new term called the charge saving score, which measures the SOC drop for each current profile relative to a reference current profile (constant maximum velocity on flat terrain). For value less than 100%, the charge consumption of that particular trip is lower than the reference profile and no further modifications to the trip are necessary. However, a value greater than 100% suggests that charge consumption is higher than the reference, which requires adjustments in choosing a new route, changing driving style, or optimizing charging strategies. Mathematically, the charge saving score is expressed as a percentage, as shown in Equation . The distribution of the charge saving score for the 108 current profiles is shown in Figure , demonstrating that all values in our dataset are less than 100%. We performed classification on 23 drive cycles to determine whether driving behaviors influence the charge saving score, using Principal Component Analysis (PCA) and K-means clustering. Figure shows the loading plot for five features extracted from the drive cycles. Among them, total deceleration, total acceleration, and braking fre- The ultimate goal of this work is to accurately predict the final SOC of an EV, considering all factors that influence SOC. For this reason, we set the charge saving score as our target output, which directly represents the final drop in SOC at the end of the trip. From figure and b, current model 1 shows relatively better prediction results with 5.1% as test MAPE, whereas the velocity model showed 7.3% MAPE (Supplementary Table ). The prediction results are only based on modeling and testing data and does not account for terrain profile or human behavior. The improved performance of the current model is attributed to the features extracted from current profiles having a stronger correlation with the charge saving score compared to that of velocity data. Supplementary Figure shows comparison of various algorithms used to develop velocity and current models.