Skip to main content

Research Repository

Advanced Search

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

Shipman, Rob; Waldron, Julie; Naylor, Sophie; Pinchin, James; Rodrigues, Lucelia; Gillott, Mark

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid Thumbnail


Julie Waldron

Sophie Naylor

Professor of Sustainable Building Design


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Vehicle‐to‐grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle‐to‐grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle‐to‐grid aggregation services.


Shipman, R., Waldron, J., Naylor, S., Pinchin, J., Rodrigues, L., & Gillott, M. (2020). Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid. Energies, 13(8),

Journal Article Type Article
Acceptance Date Apr 12, 2020
Online Publication Date Apr 14, 2020
Publication Date Apr 1, 2020
Deposit Date Apr 22, 2020
Publicly Available Date Apr 22, 2020
Journal Energies
Electronic ISSN 1996-1073
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 13
Issue 8
Article Number 1933
Keywords Vehicle-to-grid; V2G; vehicle location prediction; automated machine learning; machine
Public URL
Publisher URL


You might also like

Downloadable Citations