EVMapSim: a network-level electric vehicle charging simulator
EVMapSim: a network-level electric vehicle charging simulator
Long-distance electric vehicle (EV) travel depends critically on charging infrastructure reliability. When stations fail or queues form unexpectedly, drivers face increased range anxiety and risk of getting stranded. In this demonstration paper, we present EVMapSim, a discrete-event simulator for modelling EV navigation and charging behaviour at a national scale. We demonstrate 10,000+ vehicles traversing the UK road network, each making real-time charging decisions while encountering infrastructure failures. EVMapSim supports three failure scenarios, enabling analysis of how infrastructure resilience affects driver outcomes, including wait times, route deviations, and stranding rates.
Artifical Intelligence, Multiagent Systems, EV charging, Agent-Based Simulation, road network, Resilience
Georgiou, Prokopis
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Deshmukh, Jayati
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Yazdanpanah, Vahid
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Stein, Sebastian
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Gerding, Enrico
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Georgiou, Prokopis
a7ca3ba9-02d7-45f5-a423-c710571976b1
Deshmukh, Jayati
5903b0c1-b4d1-4fbf-b687-610d4fde3990
Yazdanpanah, Vahid
28f82058-5e51-4f56-be14-191ab5767d56
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Georgiou, Prokopis, Deshmukh, Jayati, Yazdanpanah, Vahid, Stein, Sebastian and Gerding, Enrico
(2026)
EVMapSim: a network-level electric vehicle charging simulator.
25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), , Paphos, Cyprus.
25 - 29 May 2026.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Long-distance electric vehicle (EV) travel depends critically on charging infrastructure reliability. When stations fail or queues form unexpectedly, drivers face increased range anxiety and risk of getting stranded. In this demonstration paper, we present EVMapSim, a discrete-event simulator for modelling EV navigation and charging behaviour at a national scale. We demonstrate 10,000+ vehicles traversing the UK road network, each making real-time charging decisions while encountering infrastructure failures. EVMapSim supports three failure scenarios, enabling analysis of how infrastructure resilience affects driver outcomes, including wait times, route deviations, and stranding rates.
Text
AAMAS 2026 EVMapSim _ A Network-level Electric Vehicle Charging Simulator
- Version of Record
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e-pub ahead of print date: 25 May 2026
Venue - Dates:
25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), , Paphos, Cyprus, 2026-05-25 - 2026-05-29
Keywords:
Artifical Intelligence, Multiagent Systems, EV charging, Agent-Based Simulation, road network, Resilience
Identifiers
Local EPrints ID: 509929
URI: http://eprints.soton.ac.uk/id/eprint/509929
PURE UUID: 8610d926-b6b6-4a8b-a7f1-4bc7347456d5
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Date deposited: 11 Mar 2026 17:34
Last modified: 12 Mar 2026 03:10
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