Work

Using agent-based models to simulate the electric vehicle driving behaviours in Great Britain

Year: 2026

Type: article

Abstract: With the increasing adoption rate of electric vehicles (EVs) and the green transition of transport sectors, understanding the behaviours and charging demand of EV drivers has become increasingly important, particularly for the efficient deployment and cost-effective investment of public charging stations. This paper presents an Agent-Based Model to simulate the driving and charging behaviours of EV drivers based on the trip data of England residents travelling across Great Britain. The results indicate that, despite concerns about the limited driving range of EVs, enroute charging is less necessary for drivers with short to medium distance trips, resulting in limited demand for more enroute public chargers. The presence of range anxiety among EV drivers often prompts them to charge their vehicles while parked at destinations and can further reduce the need for enroute charging. However, future expansion of the EV charging network is still necessary to accommodate the high charging demand from EV drivers undertaking long-distance trips. The simulation results can improve our understanding of EV driver behaviours and their charging demand distribution, providing insights for the future development of charging infrastructures.

Source: Cities

DOI: 10.1016/j.cities.2026.106864

Authors: Zixin Feng, Qunshan Zhao, Alison Heppenstall

Institutions: Urban Big Data Centre, University of Glasgow

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,000