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

TL;DR

This paper introduces a multi-stage reinforcement learning model to simulate private EV driver behaviour, capturing adaptive decision-making and identifying charging deserts, aiding policy development for EV infrastructure.

Contribution

It presents a novel multi-stage reinforcement learning framework that accurately models private EV driver behaviour and charging demand at a national scale, validated with real-world data.

Findings

Identified critical 'charging deserts' with low driver charging activity.

Model closely reflects actual driver behaviour and adaptive decision-making.

Highlights policy implications for expanding rapid charging infrastructure.

Abstract

Despite the rapid expansion of electric vehicle (EV) charging networks, questions remain about their efficiency in meeting the growing needs of EV drivers. Previous simulation-based approaches, which rely on static behavioural rules, have struggled to capture the adaptive behaviours of human drivers. Although reinforcement learning has been introduced 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 the gap, we propose a multi-stage reinforcement learning framework that simulates 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, which captures both the adaptive behaviours and bounded rationality of private drivers. Based on the simulation results, we also identify critical 'charging deserts' where EV drivers consistently have low state of charge. Our findings also highlight recent policy shifts toward expanding rapid charging hubs along motorway corridors and city boundaries to meet the demand from long-distance trips.