The fuel cell systems still suffer from low reliability and durability, besides the high cost. The objective of the RESYLIENT project is to develop algorithms to characterize online, realtime the operation (performance), the state of Health SoH (diagnostics) and the Remaining Useful Lifetime, RUL (Prognostics) of a PEMFC. The developed algorithms are mainly signal based using the major approach of signal processing approaches for SoH determination of the stack, online under dynamic load profiles. Besides, in automotive systems sensors are exposed to rough environmental conditions like extreme temperatures, wet and dry humidity, ice formation, vibrations, and shocks. This leads to frequent failures in sensor signals.
Objectives of the PhD thesis
The goal of this thesis is to develop simple models to increase the durability of FC systems (diagnosis, prognosis, fault tolerance) taking into account the real integration constraints of the system, in the design of the algorithms themselves; that is to say, to take into account the constraints related to embedded systems where one needs to operate a reliable diagnosis/prognosis without stopping the system, and without using intrusive, bulky, or expensive sensors or devices.
It would be interesting to apply different approaches for the analysis of non-stationary signals from PEMFC fuel cell systems to develop reliable fault tolerant diagnostics, prognosis and control. We are particularly interested in the analysis of response to multi-frequency signals. It will therefore be necessary to establish a solid theoretical basis for the signal processing tools to be used, and then apply them to databases from experimental tests. In parallel, accelerated test protocols must be set up and will allow to validate the developed algorithms.
Qualification: Master Degree
The PhD applicant should: Hold a master’s degree or equivalent and have competencies in one or several of the following topics: electrical engineering, electrochemistry, automatic control, computer sciences, Applied mathematics, data mining, artificial intelligence.
• Have good written and oral communication skills in English.
• International applications are strongly encouraged.
Deadline : 15/05/2021