Physical Model Approach for Estimating Energy Consumption of Electric Vehicle

Electric vehicles have become a global trend for reducing pollution in the transportation sector. Accurate estimation of energy consumption in electric vehicles (EVs) is essential to mitigate range anxiety and facilitate transportation system planning. This study aims to explores the feasibility of reducing computational complexity in calculating the average energy consumption (EC) by developing an EC estimation model as a mathematical function of vehicle speed, ambient temperature, and vehicle mass. We conducted simulations of an EV model based on longitudinal dynamics formulation using MATLAB/Simulink to estimate the EC. Additionally, the heating, ventilation, and air conditioning (HVAC) system details were integrated into the EV model, significantly affecting the total EC. For sensitivity analysis, the relationship between each factor and the obtained EC from the EV model was analyzed using regression methods. The proportional coefficients of mass and ambient temperature were quantified. The EC estimation model was developed as a mathematical function, and its performance was validated by comparing the relationship between speed and specific energy consumption (SEC) to the results obtained from the EV model simulation. This comparison revealed that the proposed model achieved a mean absolute percentage error (MAPE) of 4% when evaluating the correlation between speed and SEC. Therefore, we can assume that the proposed model can be further applied to preliminarily determine the minimum battery capacity suitable for various EV types.