**Rob Shipman \*, Julie Waldron, Sophie Naylor, James Pinchin, Lucelia Rodrigues and Mark Gillott**

Department of Architecture and Built Environment, Faculty of Engineering, The University of Nottingham, University Park, Nottingham NG7 2RD, UK; julie.waldron@nottingham.ac.uk (J.W.); sophie.naylor@nottingham.ac.uk (S.N.); james.pinchin@nottingham.ac.uk (J.P.); lucelia.rodrigues@nottingham.ac.uk (L.R.); mark.gillott@nottingham.ac.uk (M.G.)

**\*** Correspondence: rob.shipman@nottingham.ac.uk; Tel.: +44-1157486721

Received: 27 March 2020; Accepted: 12 April 2020; Published: 14 April 2020

**Abstract:** 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.

**Keywords:** vehicle-to-grid; V2G; vehicle location prediction; automated machine learning; machine learning
