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@PHDTHESIS{Reuscher:986972,
author = {Reuscher, Tim Andreas},
othercontributors = {Abel, Dirk and Müller, Dirk},
title = {{M}ulti-zone climatization of vehicle cabins using model
predictive control},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-05502},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2024},
abstract = {For decades, climatization systems for vehicle cabins have
been developed by both industry and academia. Especially the
rise in electric vehicles has given the topic an increased
relevance due to the requirement for energy savings.
Multi-zonal control climatization approaches, in which
multiple individually climatized zones are maintained within
the vehicle, promise increased comfort and potentially
improved efficiency. Due to the complexity of the
climatization system, its multiple input and output values,
and the slow time scales, model predictive control is a
promising approach for cabin temperature regulation.
However, the use of model predictive control for multi-zonal
control has yet tobe researched in depth. This thesis
presents an approach for the multi-zone climatization of
vehicle cabins using model predictive control. The main
contributions of this thesis are an integrated modeling
approach including all subsystems, observer designs for both
temperature and disturbance estimation, and a control
architecture exploiting both model and observer to achieve
application-oriented multi-zonal temperature control using
mixing flap and blower inputs. For the system model, a
recently published multi-zonal gray-box cabin model is
combined with low dimensional gray-box representations of
the mixing flaps and vent system. Experimental evidence is
provided to show that the vapor compression circuit can be
separated from the cabin temperature control loop without
compromising the controlsystem’s performance. Experimental
validation yields good agreement of the model with the
system behavior and a clear advantage of the proposed
gray-box model with alow-complexity white-box model.
Observers are designed to estimate cabin temperatures with
reduced sensor setups based on only a single sensor.
Multiple setups are compared. Experiments show that a single
sensor between the driver and front passenger allows to
estimate three zonal temperatures to within ±0.5K of the
actual value. A disturbance observer using a full sensor
suite is then shown to improve the prediction accuracy ofthe
derived models by $12\%-92\%.$ A model predictive control
architecture is designed based on application
considerations. It allows to separate control of the mixing
flapposition and the mass flow rate without compromising
performance or creating unwanted oscillations. A tuning
algorithm is then used to improve the robustness of the
controllerin the face of unknown disturbances or system
changes. The complete control systemis experimentally
validated, achieving individual control of two zonal
temperatures to within 0.1K in tracking and 0.3K in
worst-case disturbance rejection. The mass flowrate control
at the same time achieves comfort-oriented control of the
blower level.},
cin = {416610},
ddc = {620},
cid = {$I:(DE-82)416610_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-05502},
url = {https://publications.rwth-aachen.de/record/986972},
}
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