Replicating the behaviour of electric vehicle drivers using an agent-based reinforcement learning model

Bibliographic Data

ID21248129
AuthorsZixin Feng (0009-0003-7539-9917, University of Glasgow), Qunshan Zhao (0000-0002-5549-9457, University of Glasgow, corresponding author), Alison Heppenstall (0000-0002-0663-3437, University of Glasgow)
Year2026
Publication date2026-06-01
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/23998083261455937
OpenAlexW7163016340
LanguageEN
References cited47

Despite the rapid expansion of electric vehicle (EV) charging networks, questions remain about their efficiency in meeting the growing needs of EV drivers. Previous rule-based ABMs have struggled to capture the adaptive behaviours of human drivers. Although reinforcement learning has been applied in EV simulation studies, its application has primarily focused on optimising fleet operations rather than modelling private drivers who make independent charging decisions. To address this gap, we propose a multi-stage reinforcement learning framework that simulates the charging demand of private EV drivers across a national-scale road network. We validate the model against real-world data and identify the training stage that most closely reflects actual driver behaviour. Based on the simulation results, we identify critical ‘charging deserts’ where EV drivers face high risks of battery depletion. Our findings further highlight recent policy shifts toward expanding rapid charging hubs along motorway corridors and urban boundaries to meet growing demand from long-distance trips

Electric vehicle · Reinforcement · Reinforcement learning · Electric Vehicles and Infrastructure · Smart Grid Energy Management · Transportation and Mobility Innovations

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