Electric mobility constitutes a major section of green transport and green cities. With appropriate policies and incentives from the Governments, the number of electric passenger cars has reached more than 7 million globally. With improved battery technology and higher range, electric vehicle (EV) adoption rate has witnessed a staggering increase in the last few years worldwide, and the electric vehicle market has grown and become more competitive.
Autonomous and Intelligent charging management requires jointly considering the charging infrastructure and data from the EV users. In this regard, user privacy and data security play crucial roles in designing the data acquisition framework and charging management algorithms. We will utilize real data and realistic platforms, and distributed learning algorithms (such as federated learning) to develop a distributed architecture/framework for privacy preserved and secure charging optimisation. Within this theme, we offer two Master Thesis Topics:
- In the first topic we will develop charging management algorithms to optimise the charging costs with a particular focus on privacy preservation of vehicle users and data security. Privacy preservation includes the question of how much user data will be needed, and what limitations in user data can be handled, along with availability of personal data
Privacy compliance with privacy policies, and GDPR for the architecture will be considered.
- In the second topic, we will conduct a security evaluation of the chosen platform and communication protocols to investigate possible weaknesses with respect to security attacks and vulnerabilities. We envision a revised and more secure architecture as the outcome of this thesis.
Qualifications
Background in relevant areas of computer science such as communication and networks, Knowledge on energy systems/energy informatics, machine learning, or network security will be advantageous.
After completing the thesis, you are expected to have learnt about
Electric mobility, charging infrastructure, optimisation, machine learning, privacy preservation and data security. You will have a competence that is in high demand both in academia and in the information industry of today.