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- Electric vehicle X driving range prediction 2 EV X DRP2Publication . Valido, João Francisco Fidalgo; Ferreira, Artur Jorge; Coutinho, David PereiraAbstract The use of Electric Vehicles (EV) has increased in recent years. The autonomy of the EV, expressed as its Driving Range (DR) is a key factor. This autonomy depends on several variables related to the vehicle itself as well as with external conditions. An accurate estimation of the DR value at each moment is a challenging task. In this thesis, we address the DR estimation problem using machine learning techniques. We build a dataset with 11 features, for DR estimation, using publicly available EV data. Then, we discuss the use of Machine Learning (ML) Regression techniques to estimate DR, with Linear Regression (LR), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) neural networks. Moreover, we assess the effect of unsupervised dimensionality reduction techniques using feature selection and feature reduction approaches. The experimental results show that the use of both feature selection and feature reduction are useful at reducing the dimensionality of the data, keeping or improving the performance for DR estimation. This study also identifies the top features for DR estimation. The best feature selection method was the Mean-Median approach, while Principal Component Analysis yielded the best results in terms of feature reduction. Among the regression techniques evaluated, linear regression achieved the best overall performance. However, in real-world scenarios, where a larger number of variables may be present, methods such as MLP or RBF might offer better adaptability and robustness.