A Comprehensive Simulation of Electric Vehicle Energy Consumption: Incorporating Route Planning and Machine Learning-Based Predictions

This study presents an evaluation of predictive models for electric vehicle (EV) energy consumption in a simulated environment. Utilizing a synthetic elevation grid and considering variables such as weather conditions and traffic levels, we implemented the A* algorithm to simulate EV routes and calculate energy costs. The generated dataset was used to train three regression models: Gradient Boosting, Random Forest, and Linear Regression. Model performance was assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score. Results indicated that the predictive accuracy of the models was limited, with all models exhibiting low R2 scores and high MSE and MAE values. The study highlights the challenges in predicting EV energy consumption and suggests the need for more complex models or additional features that can better capture the dynamics of energy usage in EVs.