Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

Abstract

Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to 'learn' the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an 'anchor (baseline) based' strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of [-0.8, 0.8] as compared with previous methods.