An Experimental Comparison of Physics-based and Machine-Learning-based Electric Vehicle Energy Consumption Estimation Methods

Abstract

Electric vehicles (EVs) have gained attention in recent years due to their environmental friendliness and higher fuel efficiency. However, EV users may have concerns about their driving range. To alleviate such an anxiety, a pre-trip estimation of EV energy consumption can be helpful. There are two main approaches to predicting EV energy consumption: traditional model-based methods that use physical knowledge, and data-driven techniques that rely on machine learning methods. Although both types of methods show promise, little attention has been paid to experimentally compare their performance differences. To bridge this gap, this paper presents an experimental comparison study of three model-based and data-driven EV energy consumption estimation algorithms. Notably, real-world EV road-test datasets from urban driving are used for the comparative evaluation. Furthermore, this study offers a discussion of the pros and cons of each method, providing a guideline for algorithm improvement and selection.