Project description

The researcher would join the team in a research line related to the development of a methodology for the integration of high-fidelitymodels in virtual prototyping tools and in advanced control strategies. The developed methodology will allow the exploitation of the accuracy provided by detailed models for Li-ion batteries, fuel cells and electrolyzers during system integration of the electric propulsion plant subsystems for an aircraft and the design of new green hydrogen production plants. Detailed modelling of fuel cells and electrolysers will be used as well to develop advanced electrolysers based on new materials and innovative catalysis process design. A combination of two techniques will be used to derive accurate yet computationally efficient models for the considered electrochemical systems: reduce order modelling methods and physics-informed neural networks. Synergy of both methods (combining a sound theoretical basis and the development of efficient computer algorithms) will provide accurate models easily deployable in embedded systems, as required in the intended applications. At the same time, these efficient modelling techniques will provide the basis for advanced on-line identification algorithms.

The developed models will serve in a first stage as the basis for virtual prototyping tools to be used during system integration. At a second stage, these models will be exploited in the framework of advanced control techniques (such as nonlinear model predictive control, NMPC) exhibiting many benefits in comparison with standard, classical control techniques.

Brief description of the Centre/Research Group

Universidad Politécnica de Madrid (UPM) is Spain’s largest technological university, specializing in engineering and architecture.
As the top Spanish university participant in Horizon 2020 (100M€+ funding), UPM demonstrates a strong innovation focus, filling ~40 patents/year via its Technology Transfer Office.
The research group on modelling of electrochemical systems for energy applications at the School of Aerospace Engineering, has extensive expertise in numerical simulation, nonlinear dynamics, and reduced-order models, addressing key industrial and technological challenges. Recently, our research focus has intensified on electrochemical systems and green hydrogen production technologies.

•    Electrochemical Systems: Our work involves the development and implementation of reduced-order models to enhance computational efficiency and facilitate Lithium-ion cell optimization tools. This research has been conducted primarily within the H2020 DEFACTO project (https://defacto-project.eu).

•    Green Hydrogen Production: Our team is actively involved in the development of a virtual prototyping platform aimed at improving Proton Exchange Membrane Water Electrolysis (PEMWE) technologies. By integrating advanced components and employing AI-based control strategies, we seek to enhance system performance and drive innovation in hydrogen production. This work is being mainly conducted in the framework of an industrial research project.

The group is currently opening a new research initiative focused on integrating electric propulsion in aircraft. This work involves developing accurate and efficient models for lithium-ion batteries and fuel cells, enabling key engineering tasks such as optimizing electric power systems and implementing advanced control techniques. More information about the research group can be found at: https://portalcientifico.upm.es/es/ipublic/entity/16123 

Documents to be submitted

The ideal candidate will hold a Ph.D. in Engineering, Applied Mathematics, or Applied Physics, with a strong background in Computational Mechanics and Mathematical Modelling. Demonstrated expertise in programming languages such as Python, Fortran, and C/C++, as well as proficiency in numerical methods like the Finite Element Method (FEM) and/or the Finite Volume Method (FVM), is essential.
Preference will be given to candidates with:
•    A comprehensive understanding of electrochemical energy systems, including polymer electrolyte fuel cells and electrolyzers.
•    Knowledge of model order reduction techniques.

Applicants should submit a cover letter, resume, and a response to the selection criteria, outlining their key skills and relevant experience to support our assessment. The deadline for applications is April 30th.