Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach

TL;DR

This study uses GPS data and machine learning to analyze and forecast the daily charging demand of electric vehicles, revealing similar travel behaviors to conventional cars and providing accurate spatiotemporal demand patterns.

Contribution

It introduces a machine learning approach to forecast EV charging demand using GPS data, highlighting travel behavior similarities and improving demand prediction accuracy.

Findings

EVs and conventional vehicles have similar travel behaviors

The models accurately forecast spatiotemporal charging demand

GPS data effectively captures travel patterns for demand analysis

Abstract

The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of daily CD. Our analysis reveals that the travel behavior of EVs and conventional vehicles are similar. Also, the forecasting results indicate that the developed models can generate accurate spatiotemporal patterns of the daily CD.