Mathematical modeling of the influence of external environmental factors on the power reserve of an electric vehicle

Abstract

This article presents a mathematical modeling of the influence of external environmental factors on the power reserve of an electric vehicle. The relevance of the topic is due to the growing popularity of electric vehicles and the need to accurately predict their autonomy, taking into account real-world operating conditions. The aim of the study is to develop a comprehensive mathematical model that takes into account key environmental factors (air temperature, terrain, wind load) and analyze their impact on energy consumption and power reserve of an electric vehicle. The methodology is based on a combination of analytical modeling of physical processes and machine learning based on empirical data. Methods of mathematical physics, control theory, regression analysis, as well as original algorithms for optimizing energy consumption are used. The empirical base consists of data from field tests of 50 electric vehicles of various brands in a variety of road and climatic conditions. The following main results were obtained: 1) a detailed mathematical model of electric vehicle energy consumption has been developed, demonstrating high accuracy (the RMS error of predicting power reserve is 3.2%); 2) it has been revealed that ambient air temperature is the dominant factor, while the dependence is nonlinear (coefficient of determination is 0.92); 3) the optimal combination of motion parameters in terms of minimizing energy consumption has been determined for typical terrain profiles; 4) an adaptive algorithm for predicting power reserve is proposed, taking into account individual driving style and route (forecast error is reduced by 24% relative to the base model). The results obtained are important for the development of electric vehicle technologies, optimization of their control system and improvement of user-friendliness. In the future, it is planned to expand the model to include factors such as battery wear and seasonal changes in road conditions.