Integration of control & monitoring tools and strategies for improved Fuel Cell System durability & reliability

Expected Outcome:The transition to sustainable mobility is accelerating the adoption of hydrogen-based powertrains, integrating fuel cells, batteries, electric motors and power electronics. In particular, fuel cells (low and high temperature) represent a very promising technology for decarbonising various sectors of mobility, including maritime applications, heavy-duty vehicles and aeronautics. However, remaining aspects that hamper the large-scale diffusion of Fuel Cell Systems (FCS) include their limited lifetime and operational reliability. Moreover, higher fuel cost puts even more stringent requirements and emphasis on system efficiency.To increase the useful life and reliability of FCS, not only improvements in material and designs are required, but also on control, diagnostic, failure prediction and energy management strategies. It is thus key to integrate advanced prognostic and health management strategies into real operating FCS, suitable not only to enable the detection of anomalies, but also the real-time assessment of the current state of health, developing and integrating proper adaptive recovery strategies to mitigate FCS components’ deterioration. These considerations are generally valid for different technologies (PEMFC, SOFC…), which need to be optimally managed: all the components, both in the stack and Balance of Plant (BoP), should be continuously monitored to maximise the FCS lifetime, reliability as well as efficiency.Project results are expected to contribute to all of the following expected outcomes:Improvements of control and energy management that enable increased lifetime and operational reliability at system level, while retaining high system efficiency;Providing new product ideas and solutions addressing monitoring and diagnostics of FCS, including hardware and software;Enhanced durability and reliability of fuel cell systems, including Balance-of-Plant (BoP) components, through predictive maintenance and operational optimisation;Develop