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

Bibliographic Data

ID7472426
AuthorsZixin Feng (0009-0003-7539-9917, Urban Big Data Centre), Qunshan Zhao (0000-0002-5549-9457, Urban Big Data Centre, corresponding author), Alison Heppenstall (0000-0002-0663-3437, Urban Big Data Centre)
Year2026
Volume172
Pages106864
Publication date2026-05-01
Open AccessYes
TypeARTICLE
VenueCities (JOURNAL)
Journal identifiersISSN: 0264-2751 • E-ISSN: 1873-6084
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cities.2026.106864
OpenAlexW7128798130
LanguageEN
References cited56

With the growing adoption 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 of public charging infrastructure. While previous studies have used Agent-Based Models (ABMs) to explore these patterns, many studies rely on hypothetical networks or city-scale road systems. This limits their ability to capture long-distance, inter-city travel, which has been a key concern given EV's range limitations and limited charging infrastructure. Additionally, there is limited consideration from previous models about the behavioural heterogeneity of drivers and missing validation against real-world data. This paper presents a validated ABM that simulates the driving and charging behaviours of private EV drivers across Great Britain, incorporating diverse psychological and financial preferences, and covering both inner- and inter-city trips. 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. • An agent-based model simulates EV driving and charging patterns in Great Britain. • Drivers are more likely to charge enroute for long trips than shorter ones. • The model better represents normal days than vacation seasons.

Destinations · Driving range · Electric vehicle · Key (lock · Public transport · Range (aeronautics · Software deployment · Electric Vehicles and Infrastructure · Transportation and Mobility Innovations · Urban Transport and Accessibility

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