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Many governments have set targets and policies for zero-emission vehicles to meet global climate goals by 2050. In response to this, there has been an increase in the electrification of all major mobility providers, from private and shared cars, to busses, trucks and large commercial vehicles. For this to be operationalised at a large scale, effective charging strategies are needed to manage the network both at the Low Voltage, as well as at the Medium Voltage level. In this project, we aim to develop large-scale optimisation models to effectively charge fleets of electric vehicles.
In this research project, you will be working towards developing optimisation models that prescribe effective charging strategies for heterogeneous fleets of electric vehicles. Objectives to be modelled are, for example, the fact that the charging needs of the vehicles should be satisfied while considering the constraints imposed by the Low Voltage and Medium Voltage grid segments. For this, you will consider the power flow relations for a combined Medium-Voltage Low-Voltage network. At the same time, additional aspects such as the urgency of the charging needs and/or fairness among charging vehicles should be considered. Charging strategies are to be prescribed for a heterogeneous fleet of electric vehicles, including electric cars and shared cars, electric busses and electric trucks.
To be able to scale up the optimisation model proposed, you will also be investigating advanced decomposition methods and/or relaxation approaches for linear programming models. As input to your models, you will be analysing realistic datasets on charging profiles of various types of electric vehicles. With these data, you will analyse the performance of your optimisation models.
This PhD project is part of an European research project. As such, within the project you will have the opportunity to collaborate with other European universities and researchers. Also, as part of the PhD programme, you will have the opportunity to take technical courses on topics such as optimisation and data analysis, as well as courses on personal development.
We are looking for an enthusiastic new colleague who meets the following criteria:
We work on a better future. In order to do that, we join forces with academics, students, alumni, social partners, the government and the corporate world. Together, we look for sustainable solutions to the big challenges of today and tomorrow.
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