ISEL - Eng. Elect. Tel. Comp. - Dissertações de Mestrado

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Electric vehicle X driving range predition – EV X DRP
Publication . Albuquerque, David Alexandre Sousa Gomes; Coutinho, David Pereira; Ferreira, Artur Jorge
The electric vehicle use as a reliable and eco-friendly means of transportation has in creased rapidly over the past few years. When choosing an electric vehicle, its driving range capacity is a decisive factor to be taken into account as it minimizes driver’s anxiety while driving. An electric vehicle driving range depends on multiple factors that must be taken into account when attempting its prediction. Machine learning has become a widely used approach for highly complex problems, in which eRange prediction, being one of them, provides benefits such as becoming more accurate, the more the user drives his vehicle. This thesis compares, through standard metrics, implementations of machine learn ing based regression models (Linear regression and Ensemble Stacked Generalization) when training with publicly available datasets. The results of this work show the effects of different training sample sizes on machine learning model’s accuracy and training time, presenting more favorable results for the Linear Regression algorithm, as the algorithm was more resistant to overfitting for commonly trained data. The results can be replicated with the implemented Python application, allowing for future testing and study of the topic.
2022-12Master thesis Open access Show more