Control and Monitoring of Automotive Powertrain Systems
The transition to alternative energy sources to reduce greenhouse gas emissions poses a key challenge for the future of mobility. How can established energy sources be replaced by more environmentally friendly alternatives? Green hydrogen, produced by renewable energy sources such as solar or wind power, offers a promising solution. Hydrogen-powered fuel cell vehicles (see figure) combine long range, fast refueling, and efficient energy conversion with near-zero emissions. By utilizing green hydrogen, these vehicles can contribute to the decarbonization of transportation while maintaining the comfort and performance of traditional powertrains.
The use of fuel cells in the mobility sector, characterized by dynamic power trajectories, presents a major challenge. Frequent load changes, especially when fuel cells are not optimally controlled, can cause rapid degradation. This leads to reduced efficiency, shortened lifetime, and, in the worst case, total system failure – potentially creating safety risks. To avoid these problems, accurate and reliable control is essential. Specific criteria and constraints must be considered to ensure both high performance and long service life. Additionally, critical states, such as the temperature within the cell, are difficult or impossible to measure directly. This requires innovative approaches in control technology to operate the fuel cell efficiently and safely.
Research Topics at our Institute
By using model-based virtual sensors that estimate states using mathematical methods, we can gain insight into otherwise hidden processes. (Limited) Kalman filters and other optimization-based estimators allow to virtually glance inside the cell. A particular challenge lies in the simultaneous estimation of highly dynamic states, such as the hydrogen concentration inside the cell, together with material properties that change slowly due to aging, which have a significant impact on the fuel cell’s performance and lifetime of the fuel cell.
In the development of complex control concepts, based for example on predictive control or flatness-based approaches, the choice of model structure is critical. We use fuel cell models of varying complexity, from 0-dimensional lumped parameter models to spatially resolved multi-dimensional simulation models.
To meet real-time requirements, even for computationally intensive models, various mathematical model reduction methods are applied. These can be either data-based or model-based. This allows us to consider complex spatial distributions in control without violating real-time operational requirements.
To ensure that the models used accurately represent the behavior of the examined and controlled fuel cell systems, methods of model identification and statistical experimental design are developed and applied. This significantly reduces the measurement time required to characterize fuel cell systems.
Our research is conducted in close collaboration with academic and industrial partners to combine different competencies and work together on efficient and long-lasting fuel cells.
Publications
Bartlechner, Johanna, Martin Vrlić, Christoph Hametner, and Stefan Jakubek. "State-of-Health observer for PEM fuel cells—A novel approach for real-time online analysis., opens an external URL in a new window" International Journal of Hydrogen Energy (2024).
Vrlić, Martin, Dominik Pernsteiner, Alexander Schirrer, Christoph Hametner, and Stefan Jakubek. "Reduced-dimensionality nonlinear distributed-parameter observer for fuel cell systems., opens an external URL in a new window" Energy Reports 10 (2023): 1-14.
Du, Zhang Peng, Christoph Steindl, Stefan Jakubek, and Christoph Hametner. "Concentration estimation for fuel cells: Design of experiments, nonlinear identification, and observer design with experimental validation., opens an external URL in a new window" IEEE Access 11 (2023): 10453-10470.
Du, Zhang Peng, Christoph Steindl, and Stefan Jakubek. "Efficient two-step parametrization of a control-oriented zero-dimensional polymer electrolyte membrane fuel cell model based on measured stack data., opens an external URL in a new window" Processes 9, no. 4 (2021): 713.
Du, Zhang Peng, Andraž Kravos, Christoph Steindl, Tomaž Katrašnik, Stefan Jakubek, and Christoph Hametner. "Physically motivated water modeling in control-oriented polymer electrolyte membrane fuel cell stack models, opens an external URL in a new window." Energies 14, no. 22 (2021): 7693.
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Model-predictive-control-based reference governor for fuel cells in automotive application compared with performance from a real vehicle, opens an external URL in a new window" Energies 14, no. 8 (2021): 2206.
Böhler, Lukas, Daniel Ritzberger, Christoph Hametner, and Stefan Jakubek. "Constrained extended Kalman filter design and application for on-line state estimation of high-order polymer electrolyte membrane fuel cell systems, opens an external URL in a new window" international journal of hydrogen energy 46, no. 35 (2021): 18604-18614.
Vrlić, Martin, and Stefan Jakubek. "Degradation Avoiding Start Up and Shut Down of Fuel Cell Stacks for Automotive Application Using Two Plant Model Predictive Control, opens an external URL in a new window" In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1-6. IEEE, 2021.
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Efficient and life preserving power tracking control of a proton exchange membrane fuel cell using model predictive control, opens an external URL in a new window" In 2020 SICE International Symposium on Control Systems (SICE ISCS), pp. 77-84. IEEE, 2020
Vrlić, Martin, Daniel Ritzberger, and Stefan Jakubek. "Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data, opens an external URL in a new window" Energies 13, no. 20 (2020): 5353.
Ritzberger, Daniel, Christoph Hametner, and Stefan Jakubek. "A real-time dynamic fuel cell system simulation for model-based diagnostics and control: Validation on real driving data, opens an external URL in a new window" Energies 13, no. 12 (2020): 3148.
