Hydrogen fuel cell observer and control design for optimizing its efficiency and useful life



Scientific disciplines


Research direction

Digital Science and Technology

Affiliate site


Proton Exchange Membrane Fuel Cells (PEMFC) are one of the preferred technologies for decarbonizing the transport sector, due to their autonomy and ease of recharging. However, their large-scale democratization is hampered by their high cost, which is compounded by their unsatisfactory durability. 
The control laws of a fuel cell system must maintain a good efficiency without operating on conditions leading to potential degradation. This thesis focuses on two main objectives. First, we aim to estimate state variables that are witnesses to both efficiency and degradation. Second, based on the information given by the previous estimator, we plan to build control laws which respect the performance and safety criterion. To go further in details, the early degradation of PEMFCs is concentrated on its components, and in particular on the membrane that acts as the electrolyte, which is highly sensitive to variations in humidity and temperature during use. What's more, the distribution of these quantities across the surface of the membrane can be highly heterogeneous, making it difficult to measure with simple sensors. To anticipate potential degradation while improving performance, acceptable operating conditions in terms of humidity and temperature need to be maintained throughout the cell. We plan to estimate the stack's internal variables (humidity, temperature, quantities of materials) in real time to a satisfactory degree of accuracy. Then, we will adapt the control laws to maintain acceptable, for example, an acceptable humidity range. This thesis will contribute to the development of accurate and fast stack models and real-time control strategies in order to overcome the scientific barriers that currently prevent the sustainable use of PEMFCs.

The PhD student will develop skills in control engineering, physical modelling, computer science and optimization, and will be likely to forge close links with high-level academic and industrial partners.

Keywords: fuel cell, hydrogen, soft sensor

  • Academic supervisor    Dr Florent DI MEGLIO, CAS, ORCID : 0000-0002-0232-6130  
  • Doctoral School    ED 621, Ecole des Mines Paris 
  • IFPEN supervisor    Dr Gontran LANCE
  • PhD location    IFPEN, Lyon, France   
  • Duration and start date    3 years, starting in the fourth quarter 2024 (Novembre 4)
  • Employer    IFPEN 
  • Academic requirements    University Master degree in applied mathematics, automation, signal processing, electrochemistry  
  • Language requirements    English level B2 (CEFR)     
  • Other requirements    Good programming skills in MATLAB and/or Python, C/C++ would be welcomed 

To apply, please send your cover letter and CV to the IFPEN supervisor indicated below.

Encadrant IFPEN :
Dr Gontran LANCE