Energy Process EngineeringCopyright: © IRT
The Energy Process Engineering group is researching innovative, model-based control concepts that enable the efficient and safe operation of complex energy systems. Modern energy systems have numerous, often coupled input and output variables, so that conventional control structures do not sufficiently exploit the full potential of the systems. The Energy Process Engineering group develops solutions for such control engineering problems in the field of energy conversion. We focus on various energy conversion machines: From the control of innovative low-temperature combustion processes of combustion engines to control concepts for fuel cells and their use in mobile and stationary applications to controller developments for solar thermal power plants, our research topics cover a broad spectrum of energy systems.
One research focus of the Energy Process Engineering group is on combustion processes and combustion engines. The common features of the different types of combustion processes result in overarching challenges for control engineering:
- Combustion processes are non-linear.
- Combustion processes can be modelled physically by considering the different physical-chemical processes such as combustion chemistry, fluid mechanics and thermodynamics. However, the resulting models are too complex for an approach to application in control engineering. Therefore, these models have to be reduced for control purposes or other suitable data-driven models have to be identified.
- The dynamics of combustion processes covers several time scales. The process of chemical reactions up to the cycle of an engine covers several time scales.
Together with industry and university partners, we are conducting research on modern, model predictive control methods for combustion processes that go far beyond the methods commonly used in industrial practice. A further focus is on the real-time-capable implementation of the algorithms on rapid-control-prototyping hardware and experimental testing on test benches or in prototype vehicles.
- Application of real-time capable nonlinear model predictive controls. Here, the nonlinear behavior can be systematically taken into account and at the same time, in addition to the follow-up control, the compliance with limit values can also be taken into account.
- Combination of model predictive and iterative learning controls for cyclic processes.
- Nonlinear black box identification for process components that are difficult to model
- Creation of control-oriented white-box models.
- Nonlinear observer concepts to estimate the non-measurable quantities quickly and reliably.
Fuel Cell / Energy Storage Operation
To compensate for fluctuations in the energy supply, storage systems will become increasingly important in the future. Decisive for their economic use are control strategies that take into account both technical and economic aspects, such as price fluctuations during operation. The development of such control procedures for optimal storage operation is another research focus of the Energy Process Engineering group. In particular, the possibilities of a dynamic operation of the fuel cell are investigated.
In principle, storage systems are used in a wide variety of applications: in industrial processes, in regenerative power plants or at central nodes in the electrical grid. What these applications have in common is that the control system must fulfil at least the following two tasks:
- Technical task: Compensate energy deficits
- Economic task: Minimize operating costs
Storage systems and the associated control task are characterized by the following properties:
- In many cases, the stored energy can be reused in the different consumption sectors electricity, heat or mobility, so that multi-volume systems result.
- Depending on the technology, storage systems represent nonlinear or distributed processes. Electrolysis storage systems, for example, are highly nonlinear, whereas thermal storage systems are best described by partial differential equations.
- The control problem is determined by stochastic variables, such as future price, feed-in or usage data. Applied control methods must be able to handle these uncertainties.
- The purely technical view of the system is not conducive to the goal and must always be supplemented by economic aspects.
In cooperation with experts in the fields of storage technologies and energy systems, the Energy Process Engineering group is researching modern control procedures that meet the above-mentioned requirements. The starting point for such work are calculations based on models of future energy scenarios, such as those underlying the "Energy Concept 2050". Our activities are mainly concentrated on:
- The development of control concepts that can handle economic and technical aspects of operation. Due to the different time constants of these processes, multi-level concepts are pursued.
- The development of physical white-box models of storage and energy systems and systematic reduction of these for use in real-time applications.
- The development of adaptive model predictive controls as well as mixed-integer and nonlinear optimizations in order to optimize storage operation in real time.
- The use of uncertain and limited price and feed-in data forecasts with the aim of developing control procedures of practical relevance and generating reliable statements on the feasibility of energy scenarios.
As an alternative to purely battery-based powertrains, the Energy Process Engineering group is also investigating the control of the fuel cell itself. Besides the nonlinear process dynamics, the strongly varying time scales also pose special control engineering challenges. Therefore, hierarchical, nonlinear model predictive controls are used for the dynamic power supply, which explicitly consider the operating limits of the fuel cell and the auxiliary units necessary for air supply. Thus, a higher overall efficiency can be achieved compared to quasi-static operation. For the validation of the procedures, the institute’s fuel cell hybrid test bench is used, on which the underlying models can be validated.
Solar-Thermal Power Plants
In the field of solar thermal power plants, the Energy Process Engineering Group is involved in the control of concentrated solar power, the CSP, systems such as the solar tower power plant. Here, the sunlight is focused on a receiver at the top of the tower using several thousand mirrors. The high heat flux densities that are created in this process can be used in a variety of ways, e.g. to generate electricity via a thermal power process or to produce hydrogen in a chemical process.
The control challenges
- lie in maximizing the yield while maintaining permissible limit temperatures and temperature gradients, which can lead to mechanical stresses and material damage. In particular, cloud passages with strongly varying radiation power are critical operating conditions.
- The systems are nonlinear and distributed-parametric.
- With the distribution of the solar radiation through the mirrors and the setting of a mass flow in the receiver, it is a system with several control variables.
- A cloud camera system provides a short-term forecast of solar radiation by observing clouds, which has to be considered as information with uncertainties.
In order to solve the resulting problems, research is being conducted on the application of various methods:
- Model-based predictive control and application of robust methods
- Use of observers to estimate non-measurable critical system states
- Design of reduced, real-time capable white-box and black-box models
- Distribution parametric design methods and model reduction methods
Project-, Bachelor- or Master-thesis and student jobs
We are always looking for motivated students who want to work in the above mentioned areas within their project-, bachelor- or master-thesis or are looking for a student job. You are very welcome to send us your application.
Selected ongoing projects
- Control of GCAI Combustion Engines
- Rate Shaping for Diesel Engines
- Air Path Control for Two-Stage Turbocharged Gasoline Engines with Exhaust Gas Recirculation
- Energy Management for Hybrid Diesel Vehicles
- DynaSalt-2 – Control of the transient single-phase operation of molten salt receivers
- Integrated Energy Supply Modules for Roadbound E-Mobility - mobilEM
- Dynamic Modeling and Model-Based Control of PEM Fuel Cell Systems - DynaCell