Control and Monitoring of Automotive Powertrain Systems
The shift away from fossil fuels as an energy source for mobile applications to reduce greenhouse gases leads to interesting questions: How can familiar and convenient energy sources be replaced? How are they obtained? As an environmentally friendly energy carrier, green hydrogen offers a solution to current problems. Electric vehicles powered by fuel cells allow long ranges, fast refill, efficient use of energy and, moreover, guarantee almost CO2-free operation. The hydrogen required can be obtained from solar or wind energy, for example, and then used for sustainable mobility.
However, the use of a fuel cell in the mobility sector in particular, with the usual highly dynamic power requirements, poses a major challenge here. Frequently changing loads cause fuel cells to degrade rapidly, especially if they are not optimally controlled, which can lead to reduced performance, deteriorating efficiency or system failure. Therefore, it is necessary to investigate which criteria and constraints have to be met and taken into account by the control concept. This is complicated by the fact that many relevant variables such as temperature within the cell can only be measured with great effort or not at all.
For this reason, we at the institute are working on the use of model-based virtual sensors, which can estimate states using mathematical methods and thus provide insight into otherwise hidden correlations. Furthermore, it is important to consider the model complexity for the control, since real-time requirements must not be violated in real operation.
Research Topics at our Institute
The aforementioned problems are addressed at our institute, among others. Different control concepts like model predictive control or flatness based approaches are applied and advantages and disadvantages are analyzed. Furthermore, the model structure is a decisive factor for the controller design. Here, we use fuel cell models of different complexity levels, from 0-dimensional lumped parameter models to spatially discretized multi-dimensional simulation models. To ensure real-time requirements even for the computationally expensive models, various mathematical model reduction techniques are used.
These can be either data-driven or model-based. Last but not least, state estimators also play a major role, which enable the use as a real-time system. Besides (constrained) Kalman filters, optimization-based estimators are also used. In our research, we work closely with academic and industrial partners in order to jointly work on efficient and long-lasting fuel cells by combining a wide range of competences.
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.