Estimating Electric Vehicle Driving Range with Machine Learning

Abstract

In the past years, we have witnessed an increase on the use of electric vehicles (EV), which are now widely accepted as reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key parameters of choice for the consumer is its driving range (DR) capability. The DR depends on many factors that should be addressed when predicting its value. In some cases, the existing heuristic techniques for DR estimation provide values with large variation, which may cause driver anxiety. In this paper, we explore the use of machine learning (ML) techniques to estimate the DR. From publicly available data, we build a dataset with EV data suitable to estimate the DR. Then, we resort to regression techniques on models learned on the dataset, evaluated with standard metrics. The experimental results show that regression techniques perform adequate and smooth estimation of the DR value on both short and long trips, avoiding the need to use the previous heuristic techniques, thus minimizing the drivers anxiety and allowing better trip planning.