Enhancing Electric Vehicle Energy Consumption Prediction: Integrating Elevation into Machine Learning Model

The energy consumed to overcome gravity during elevation gain is a significant factor in the energy consumption of electric vehicles (EVs). Assessing elevation influence can help improve the accuracy of estimated energy consumption, which will alleviate drivers' range anxiety. This study explores how to improve the accuracy of energy consumption prediction for EVs using elevation features. The trip dataset is supplemented with elevation features, and then a voting ensemble model of machine learning is proposed to predict energy consumption. Also, a total of 10,847 trip records from 16 hilliness cities and 13 flatness cities in the United States are studied. The experimental results show that the prediction accuracy of EVs energy consumption improves with the inclusion of elevation features, where the Mean Absolute Error (MAE) of the prediction result decreases from 796 Wh to 695 Wh, and the R-squared (R-2) score of the prediction result increases by 1.6% to finally reach 94.4%.