Fuel cells are a key technology for clean and sustainable energy systems, particularly in hybrid configurations for transport and stationary applications. However, their durability under real-world conditions remains a critical challenge. This project aims to address these challenges by exploring advanced control strategies that leverage state-of-the-art prognostic algorithms for fuel cell health assessment.
This postdoc offer focalizes on Advanced Control and Optimization, specifically the design of Fault-Tolerant Control (FTC).
Building on prior work in machine learning-based prognostics for fuel cell health, the focus of this project is to develop methods to utilize this prognostic information to optimize the operation of the fuel cell system.
By incorporating both model-based, data-driven approaches and testing on real test-bench platforms, the project aims to create robust, deployable solutions that enhance fuel cell durability while reducing the complexity and cost of implementation.