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Assessing Dimensionality Reduction on Driving Range Estimation
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AI
DOI：10.1007/978-3-031-99565-1_24.png)
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Abstract
En 中文
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 paper, 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 a few top features for DR estimation.
Keywords:
Dimensionality Reduction
Driving Range Estimation
Electric Vehicle
Feature Reduction
Feature Selection
Machine Learning
Neural Networks
Regression
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Papers:
35
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