| dc.description.abstract | In the present thesis different SOFC stack models have been presented.
The results shown were obtained in the general framework of the GENIUS
project (GEneric diagNosis Instrument for SOFC systems), funded by the
European Union (grant agreement n° 245128). The objective of the project
is to develop “generic” diagnostic tools and methodologies for SOFC
systems. The “generic” term refers to the flexibility of diagnosis tools to be
adapted to different SOFC systems.
In order to achieve the target of the project and to develop stack models
suitable for monitoring, control and diagnosis applications for SOFC
systems, different modeling approaches have been proposed. Particular
attention was given to their implementability into computational tools for
on-board use. In this thesis one-dimensional (1-D), grey-box and blackbox
stack models, both stationary and dynamic were developed. The
models were validated with experimental data provided by European
partners in the frame of the GENIUS project.
A 1-D stationary model of a planar SOFC in co-flow and counter-flow
configurations was presented. The model was developed starting from a 1-
D model proposed by the University of Salerno for co-flow configuration
(Sorrentino, 2006). The model was cross-validated with similar models
developed by the University of Genoa and by the institute VTT. The crossvalidation
results underlined the suitability of the 1-D model developed. A
possible application of the 1-D model for the estimation of stack
degradation was presented. The results confirmed the possibility to
implement such a model for fault detection.
A lumped gray-box model for the simulation of TOPSOE stack thermal
dynamics was developed for the SOFC stack of TOPSOE, whose
experimental data were made available in the frame of the GENIUS
project. Particular attention was given to the problem of heat flows
between stack and surrounding and a dedicated model was proposed. The
black-box approach followed for the implementation of the heat flows and
its reliability and accuracy was shown to be satisfactory for the purpose of
its applications. The procedure adopted turned out to be fast and applicable
to other SOFC stacks with different geometries and materials. The good
results obtained and the limited calculation time make this model suitable
for implementation in diagnostic tools. Another field of application is that
of virtual sensors for stack temperature control.
Black-box models for SOFC stack were also developed. In particular, a
stationary Neural Network for the simulation of the HEXIS stack voltage
was developed. The analyzed system was a 5-cells stack operated up to 10
thousand hours at constant load. The neural network exhibited very good
prediction accuracy, even for systems with different technology from the
one used for training the model. Beyond showing excellent prediction
capabilities, the NN ensured high accuracy in well reproducing evolution
of degradation in SOFC stacks, especially thanks to the inclusion of time
among model inputs. Moreover, a Recurrent Neural Network for dynamic
simulation of TOPSOE stack voltage and a similar one for a short stack
built by HTc and tested by VTT were developed. The stacks analyzed
were: a planar co-flow SOFC stack (TOPSOE) and a planar counter-flow
SOFC stack (VTT-HTc).
All models developed in this thesis have shown high accuracy and
computation times that allow them to be implemented into diagnostic and
control tool both for off-line (1-D model and grey-box) and for on-line
(NN and RNNs) applications. It is important noting that the models were
developed with reference to stacks produced by different companies. This
allowed the evaluation of different SOFC technologies, thus obtaining
useful information in the models development. The information underlined
the critical aspects of these systems with regard to the measurements and
control of some system variables, giving indications for the stack models
development.
The proposed modeling approaches are good candidates to address
emerging needs in fuel cell development and on-field deployment, such as
the opportunity of developing versatile model-based tools capable to be
generic enough for real-time control and diagnosis of different fuel cell
systems typologies, technologies and power scales. [edited by author] | en_US |
