Detection of undeclared EV charging events in a green energy certification scheme
File(s)
Accepted version
Author(s)
Loiacono, Luca Domenico
Quinn, Anthony
Crisostomi, Emanuele
Shorten, Robert
Type
Journal Article
Abstract
The green potential of electric vehicles (EVs) can be fully realized only if their batteries are charged using energy generated from renewable (i.e. green) sources. For logistic or economic reasons, however, EV drivers may be tempted to avoid charging stations certified as providing green energy, instead opting for conventional ones, where only a fraction of the available energy is green. This behaviour may slow down the achievement of decarbonisation targets of the road transport sector. In this paper, we use GPS data to infer whether an undeclared charging event has occurred. Specifically, we construct a Bayesian hypothesis test for the charging behaviour of the EV. Extensive simulations are carried out for an area of London, using the mobility simulator, SUMO, and exploring various operating conditions. Excellent detection rates for undeclared charging events are reported. We explain how the algorithm can serve as the basis for an incentivization scheme, encouraging compliance by drivers with green charging policies.
Date Issued
2026-08-01
Date Acceptance
2026-07-09
Citation
IEEE Transactions on Intelligent Vehicles, 2026, 11 (8), pp.946-960
ISSN
2379-8858
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
946
End Page
960
Journal / Book Title
IEEE Transactions on Intelligent Vehicles
Volume
11
Issue
8
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2026-07-15