Impact
The project will develop an algorithmic toolkit for analysis and synthesis of charging control strategies in large populations of electric vehicles, addressing two key issues that are faced by the industry:
dealing with shared resource constraints, as it is most often the case in situations of
practical relevance;
handling uncertainty in an efficient, albeit rigorous, manner.
These contributions will facilitate future application and experimental validation of the developed electric vehicle charging control strategies by the energy and transportation industrial sector, and accommodate vehicle populations of growing size.
The following impact criteria are addressed:
Economic×Performance improvement in electric vehicle charging control methods, leading to charging cost savings, intelligent resource
allocation and facilitating the integration of higher shares of renewable energy sources. These features will make the developed algorithms
appealing to electric vehicle charging network operators, and to leading industrial partners in energy efficient management technologies
and smart-grid solutions.
Societal×Robustness in charging schedules enhancing reliability. This will increase the confidence of electric vehicle users and
provide cost efficient solutions, while respecting their privacy concerns regarding their consumption patterns.
Knowledge & Skills×Developing novel solutions for control of multi-agent systems, merging tools from different disciplines, such as
control theory, optimization and machine learning. This will set the ground for breakthrough solutions to the problem of control and
performance optimization in uncertain environments with multiple agents.
Users×Direct engagement with industrial representatives from the energy and transportation sector, exploiting support by Siemens and
Honeywell. Development of an open access algorithmic repository, allowing for benchmarking and result reproducibility, supported by
MathWorks.
