Interval Methods for Real-Time Capable Robust Control of Solid Oxide Fuel Cell Systems

Rauh, Andreas; Senkel, Luise; Auer, Ekaterina; Aschemann, Harald

doi:10.1007/s11786-014-0205-x

Cited by 6 publications

(11 citation statements)

References 14 publications

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“…In [1,2], the modelling principle for the thermal subsystem of SOFCs was described. It is based on general techniques presented, for example, in [11], and is adjusted to fit the actual SOFC test rig.…”

Section: Models For Sofc and Their Verification Degreementioning

confidence: 99%

“…These ODEs are used as the basis for the subsequent simulation and control of the SOFC stack. In contrast to the methodology in [12], where the system model is simplified even further by a linearisation, the approach described in [1,2] leads to system representations that are valid for large operating domains by accounting for the most relevant nonlinear phenomena. In the third step, the parameters of the developed ODE models are identified using least squares minimization (or, in case of interval methods, global optimisation) with the measured data from the SOFC test rig.…”

Section: Models For Sofc and Their Verification Degreementioning

confidence: 99%

“…Since 2010, the ongoing project VerIPC-SOFC has been dealing with modelling, simulation, and control of solid oxide fuel cell (SOFC) systems. During this time, highly efficient models for the thermal subsystem (consisting of the fuel cell stack and the anode and cathode gas preheaters) have been developed, verified, and validated on a test rig available at the Chair of Mechatronics at the University of Rostock [1,2]. The general goal of the project is to develop robust control methods that prevent over-temperatures in the stack by taking into account uncertainty in parameters, measurements and unknown influences on the system dynamics with the help of interval arithmetics.…”

Section: Introductionmentioning

confidence: 99%

“…The general goal of the project is to develop robust control methods that prevent over-temperatures in the stack by taking into account uncertainty in parameters, measurements and unknown influences on the system dynamics with the help of interval arithmetics. Our methods make use of sliding mode techniques [3,4] and of predictive control approaches [1].…”

Section: Introductionmentioning

confidence: 99%

“…This includes a brief overview of the general modelling process for the temperature in Section 2.1 and of methods with result verification in Section 2.2. For more details on these topics, refer to, for example, Rauh et al [1,2] and Moore et al [8]. After explaining the specifics of the V&V process using methods with result verification in Section 2.3, we apply it to control-oriented models of the SOFC temperature in Section 2.4.…”

Section: Introductionmentioning

confidence: 99%

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Control-Oriented Models for SO Fuel Cells from the Angle of V&V: Analysis, Simplification Possibilities, Performance

et al. 2017

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In this paper, we take a look at the analysis and parameter identification for control-oriented, dynamic models for the thermal subsystem of solid oxide fuel cells (SOFC) from the systematized point of view of verification and validation (V&V). First, we give a possible classification of models according to their verification degree which depends, for example, on the kind of arithmetic used for both formulation and simulation. Typical SOFC models, consisting of several coupled differential equations for gas preheaters and the temperature distribution in the stack module, do not have analytical solutions because of spatial nonlinearity. Therefore, in the next part of the paper, we describe in detail two possible ways to simplify such models so that the underlying differential equations can be solved analytically while still being sufficiently accurate to serve as the basis for control synthesis. The simplifying assumption is to approximate the heat capacities of the gases by zero-order polynomials (or first-oder polynomials, respectively) in the temperature. In the last, application-oriented part of the paper, we identify the parameters of these models as well as compare their performance and their ability to reflect the reality with the corresponding characteristics of models in which the heat capacities are represented by quadratic polynomials (the usual case). For this purpose, the framework UNIVERMEC (Unified Framework for Verified GeoMetric Computations) is used, which allows us to employ different kinds of arithmetics including the interval one. This latter possibility ensures a high level of reliability of simulations and of the subsequent validation. Besides, it helps to take into account bounded uncertainty in measurements.

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Section: Models For Sofc and Their Verification Degreementioning

confidence: 99%

Section: Models For Sofc and Their Verification Degreementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%