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Estimating Electric Vehicle Driving Range with Machine Learning

Albuquerque, D. ; Ferreira, A. ; Antão, DPC

Estimating Electric Vehicle Driving Range with Machine Learning, Proc INSTICC International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Lisboa, Portugal, Vol. , pp. 336 - 343, February, 2023.

Digital Object Identifier: 10.5220/0011672100003411


n 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