Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most, if not all, electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power through a linear relationship. A base model and three normalized models are developed by fitting the UDDS and HWFET energy consumption test data published by the EPA for all BEVs in the U.S. market. Out of the models analyzed, the normalization with weight performs best with the lowest RMSE values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWY, and US06 data points, respectively, and 0.653 kW for all three data sets combined. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in RMSE for predictions with all data sets combined. Removing outliers in addition to consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in RMSE for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles. / Master of Science / Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from conventional internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired to support quick, high-level analyses for BEVs without requiring expensive resources. Therefore, the goal of this paper is to create a simple vehicle model that can estimate the energy consumption of most, if not all, electric vehicles by scaling the data using vehicle parameters. These parameters include the vehicle test weight and performance to obtain a unified net Willans line model describing the input/output power through a linear relationship. The UDDS (city) and HWFET (highway) energy consumption data points used to develop the model are easily accessible from published EPA data. Out of the models analyzed, the normalization with test weight performs best with the lowest error values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWFET, and US06 (aggressive city/highway cycle) data points, respectively, and 0.653 kW for all three data sets combined. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in error for predictions with all data sets combined. Removing outliers in addition to consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in error for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles.