Periodic Reporting for period 1 - RUBY (Robust and reliable general management tool for performance and dUraBility improvement of fuel cell stationarY units)
Okres sprawozdawczy: 2020-01-01 do 2022-01-31
Fuel Fells (FCs) are becoming more and more attractive for real world applications, their performance meet the strong constraints imposed by regulations and international policies aiming at reducing the carbon footprint of power systems. Despite the noticeable improvements attained in the last decade, still the competitiveness with respect to conventional solutions is yet to be achieved for FCs efficiency, reliability, availability and durability. The implementation on FC Systems of suitable control, diagnostic and prognostic algorithms can improve those performance, making FC cell more efficient and competitive. The RUBY project covers the areas of Proton Exchange Membrane FC (PEMFC) backup and Solid Oxide FC (SOFC) µ-CHP systems that are among the most mature stationary FC solutions available on the market today.
The project aims at developing, integrating, engineering and testing a comprehensive and generalized Monitoring, Diagnostic, Prognostic and Control (MDPC) tool capable of improving efficiency, reliability and durability of SOFC and PEMFC systems for stationary applications. The MDPC tool features Electrochemical Impedance Spectroscopy-based stack monitoring for fault diagnosis and lifetime estimation, Balance of Plant diagnostics, supervisory Real Time Optimization (RTO) control and mitigation strategies. The tool relies on advanced dedicated hardware and will be embedded in the Fuel Cell Systems (FCSs) for on-line validation in relevant operational environment.
Therefore, the RUBY tool will reach at the end of the project a Technology Readiness Level equal to TRL 7. It is foreseen that the tool will be ready for engineering scaling up of production, together with certification for embedding within commercial FCS.
To successfully fulfil the aims of RUBY, the following four main technical objectives were set:
1. Improve FCS performance and durability by implementing an advanced and integrated algorithm that combines monitoring, diagnosis, prognosis, control and mitigation actions for both SOFC and PEMFC systems.
2. Design and engineer the hardware required for MDPC algorithms application, with attention to sensors reduction issues and the specific constraints imposed by stack technologies and systems applications towards industrial scalability.
3. Perform dedicated experimental campaigns for stacks and system characterization and MDPC tool prototype validation embedded on FCSs running in operational environment.
4. Develop an advanced FCS management strategy (supervisory level), with functionalities integrated with remote monitoring, for future smart-grid interaction and predictive maintenance application.
The project aims at developing, integrating, engineering and testing a comprehensive and generalized Monitoring, Diagnostic, Prognostic and Control (MDPC) tool capable of improving efficiency, reliability and durability of SOFC and PEMFC systems for stationary applications. The MDPC tool features Electrochemical Impedance Spectroscopy-based stack monitoring for fault diagnosis and lifetime estimation, Balance of Plant diagnostics, supervisory Real Time Optimization (RTO) control and mitigation strategies. The tool relies on advanced dedicated hardware and will be embedded in the Fuel Cell Systems (FCSs) for on-line validation in relevant operational environment.
Therefore, the RUBY tool will reach at the end of the project a Technology Readiness Level equal to TRL 7. It is foreseen that the tool will be ready for engineering scaling up of production, together with certification for embedding within commercial FCS.
To successfully fulfil the aims of RUBY, the following four main technical objectives were set:
1. Improve FCS performance and durability by implementing an advanced and integrated algorithm that combines monitoring, diagnosis, prognosis, control and mitigation actions for both SOFC and PEMFC systems.
2. Design and engineer the hardware required for MDPC algorithms application, with attention to sensors reduction issues and the specific constraints imposed by stack technologies and systems applications towards industrial scalability.
3. Perform dedicated experimental campaigns for stacks and system characterization and MDPC tool prototype validation embedded on FCSs running in operational environment.
4. Develop an advanced FCS management strategy (supervisory level), with functionalities integrated with remote monitoring, for future smart-grid interaction and predictive maintenance application.
The achievements of the project RUBY at end of Periodic Report No 1 are:
- EIS board for Backup system ready.
- Experimental test protocols closed for stacks characterization.
- PEMFC stack and systems installed on test rigs under testing.
- SOFC stacks at lab premises ready for experiments to start.
- Two SOFC systems being connected to instrumentations for testing.
- Methodologies identified for MDPC functions.
- Preliminary tests for monitoring and prognostics algorithms for both PEM and SOFC.
The monitoring and diagnostic algorithms of the Balance of Plants are being developed relying on conventional fault detection and isolation approaches making use of lumped models for monitoring; a sensors fault detection algorithm has been proposed as well. Stack monitoring and diagnostics is performed through EIS data with several approaches based on Equivalent Circuit Models and both Soft Computing and Artificial Intelligence methodologies. Prognostic algorithms are implemented via both Artificial Intelligence and Sensor Data Fusion techniques and are tested on long run field data. The (RTO) controller is under development for the µ-CHP system.
For the implementation of the EIS-based monitoring on-board of both FC systems the hardware has been designed to generate and superimpose on the stack current either sinusoidal or Pseudo Random Binary Signals to cause a time variation of the voltage. Two configurations (see figure 1) have been designed; in the first one the MDPC tool controls the converter to generate the signal perturbation, whereas in the other a dedicated hardware will impose the perturbation, while keeping the inverter as it is. A compact prototype of the MDPC board has been also built.
The experimental campaign on both FC systems has started to generate data for the validation of the algorithms. Tests are progressing for a PEM stack in nominal and faulty conditions and for two backup systems (figure 2). For SOFC, one stack will be tested along with two µ-CHP (figure 3), all tests will be performed in nominal and faulty operations.
Advanced functions are being developed to implement energy management systems that would help achieving the optimal use of the energy within smart grids. A hierarchical control architecture (figure 4) has been proposed for the best exploitation of RUBY MDPC tool; it will help in managing the interactions among FCs and other energy systems within a smart grid making use of virtual power plant (VPP) concept as well.
- EIS board for Backup system ready.
- Experimental test protocols closed for stacks characterization.
- PEMFC stack and systems installed on test rigs under testing.
- SOFC stacks at lab premises ready for experiments to start.
- Two SOFC systems being connected to instrumentations for testing.
- Methodologies identified for MDPC functions.
- Preliminary tests for monitoring and prognostics algorithms for both PEM and SOFC.
The monitoring and diagnostic algorithms of the Balance of Plants are being developed relying on conventional fault detection and isolation approaches making use of lumped models for monitoring; a sensors fault detection algorithm has been proposed as well. Stack monitoring and diagnostics is performed through EIS data with several approaches based on Equivalent Circuit Models and both Soft Computing and Artificial Intelligence methodologies. Prognostic algorithms are implemented via both Artificial Intelligence and Sensor Data Fusion techniques and are tested on long run field data. The (RTO) controller is under development for the µ-CHP system.
For the implementation of the EIS-based monitoring on-board of both FC systems the hardware has been designed to generate and superimpose on the stack current either sinusoidal or Pseudo Random Binary Signals to cause a time variation of the voltage. Two configurations (see figure 1) have been designed; in the first one the MDPC tool controls the converter to generate the signal perturbation, whereas in the other a dedicated hardware will impose the perturbation, while keeping the inverter as it is. A compact prototype of the MDPC board has been also built.
The experimental campaign on both FC systems has started to generate data for the validation of the algorithms. Tests are progressing for a PEM stack in nominal and faulty conditions and for two backup systems (figure 2). For SOFC, one stack will be tested along with two µ-CHP (figure 3), all tests will be performed in nominal and faulty operations.
Advanced functions are being developed to implement energy management systems that would help achieving the optimal use of the energy within smart grids. A hierarchical control architecture (figure 4) has been proposed for the best exploitation of RUBY MDPC tool; it will help in managing the interactions among FCs and other energy systems within a smart grid making use of virtual power plant (VPP) concept as well.
The project preliminary outcomes are confirming the potential in generating positive impacts on both technology and market fields, thus contributing to the progress of FCSs products beyond the state-of-the-art. The results achieved so far confirm the expected advancements though a complete assessment will be performed in the second part of the action. One interesting result deals with the design of two hardware configurations for EIS perturbation. In both cases the flexibility of the solutions can guarantee the implementation of EIS-based monitoring on any FC system. Furthermore, with respect to the project proposal, additional algorithms have been developed; this result will enlarge the possibility to find more appropriate and effective techniques for diagnostics, prognostics and control to enhance the lifetime while keeping the efficiency closer to that at beginning of life (BOL).