Doctoral Thesis Sustainable Electric Vehicle Management using Coordinated Machine Learning

Defended on Thursday, 30 June 2016


The purpose of this dissertation is to investigate how intelligent algorithms can support electricity customers in their complex decisions within the electricity grid. In particular, we focus on how electric vehicle (EV) owners can be supported in their charging and discharging decision, benefiting from the information available. We examine the problem from different standpoints and show the benefits for each involved stakeholder dependent on the market conditions.

In the first essay, we take the perspective of an individual EV owner and design an intelligent algorithm, which learning from her preferences and driving and consumption information, proposes optimized charging and discharging recommendations.

In the second essay, we extend the first one by incorporating the EV within a smart home with a photovoltaic panel. The main goal of this study is to examine how accurate solar generation forecasting can be useful for charging the EV and make the best out of renewable sources. We propose a supervised learning algorithm which estimates the solar generation output from the weather conditions.

In the third essay, we examine problem from the grid operator’s point of view, taking a top-down approach. We propose an auction mechanism which has as its main goal to service as many EV owners as possible, given a certain grid capacity.

In the fourth essay, we propose a hybrid mechanism which combines benefits from top-down and bottom-up approaches. This mechanism is based on dynamic price functions that are able to incentivize EV customers to delay their charging duration when there is no urgency.

Overall, this dissertation contributes to the academic literature with new algorithms that can leverage the power of data available and personalize EV charging recommendations. It also contributes to practice by providing useful insights to the involved stakeholders such as grid operators, energy utility companies, individual customers and automotive companies with respect to creating the right incentives for EV adoption. Finally, it adds to the very important discussion about sustainability, since it proposes ways to reduce carbon footprint and benefit the most from the available renewable sources.


Electric vehicles, coordination, machine learning, algorithmic design, energy informatics, sustainability, smart charging

Time frame

2011 - 2016

Preferred reference

K. Valogianni, Sustainable Electric Vehicle Management using Coordinated Machine Learning, Eric van Heck,


Konstantina Valogianni
Konstantina Valogianni

Supervisory Team

Eric van Heck
Professor of Information Management and Markets
  • Promotor
Wolfgang Ketter
Professor of Next Generation Information Systems
  • Daily Supervisor

Committee Members

Jan van Dalen
Associate Professor of Statistics
René de Koster
Professor of Logistics and Operations Management
Andreas Symeonidis
Andreas Symeonidis
Rainer Unland
Rainer Unland