The ever increasing market penetration of electrified vehicles, the diffusion of intermittent renewable energy sources, together with the growing public interest in environmental issues, are acting as a catalyst for research on smart-grid energy management. It is nowadays widely acknowledged that the growing number of electrified vehicles can cause very high peak electricity demand, which needs to be properly addressed and managed in order to avoid electricity demand/supply unbalance and system breakdown. To this aim, stationary energy storage and “smart charging” represent two of the most promising strategies to increase grid resilience and flexibility.
The optimization objective consists in managing the electric vehicles charge and verifying the constraint on the maximum power absorption.
The advent of connected vehicles offers new opportunities of effectively controlling the charging process. In particular, information about current battery state-of-charge, planned trip and traffic conditions, could be used to accurately predict the initial level of charge when plugging the vehicle at the charging station. Furthermore, learning from typical vehicle use and traveling patterns may help to estimate the necessary level of battery charge to complete the forthcoming trip. The integration of this kind of information in the charging optimization problem is one of the main objectives of this thesis.
Therefore, a stochastic traffic model is envisaged in order to estimate the energy needs of a sample fleet of electric vehicles and thus to control the charging stations, as well as the stationary energy storage. Constraints coming from the time-varying available power at the charging stations, due to hourly limitations and/or intermittent power suppliers, shall be considered.
The developed control and optimization strategies shall be tested in a microscopic traffic simulator to assess the performance and the robustness of the proposed method.
Keywords: Optimization, smart electric grid, batteries, electric vehicles, connected vehicles
- Academic supervisor Prof. Maria Domenica Di Benedetto, University of L’Aquila (Italy)
- Doctoral School De L’Aquila University and ED STIC (Sciences et Technologies de l'Information et de la Communication), http://edstic.unice.fr/en
- IFPEN supervisor Dr. Philippe Pognant-Gros, Control, Signal and System Department, email@example.com
- PhD location IFP Energies nouvelles, Lyon, France
- Duration and start date 3 years, starting not earlier than September 2020
- Employer IFP Energies nouvelles, Lyon, France
- Academic requirements University Master degree in Electrical Engineering, specialization in Automation
- Language requirements Fluency in English. willingness to learn French is a plus
- Other requirements Good programming skills, Matlab/Python
IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see https://www.ifpen.com.
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.