% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Griefnow:834347,
author = {Griefnow, Philip},
othercontributors = {Andert, Jakob Lukas and Abel, Dirk},
title = {{N}ichtlineare modellprädiktive {R}egelung von
{M}ild-{H}ybridantrieben mit elektrischer {Z}usatzaufladung},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2021-09813},
pages = {1 Online-Ressource : Illustrationen},
year = {2021},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2021},
abstract = {In the context of a strongly increasing 48V electrification
this thesis takes up the special challenges of the
powertrain management of 48V mild hybrid powertrains with
electric supercharging and presents a model predictive
control concept, which is able to improve the response
behaviour and the fuel consumption compared to
state-of-the-art heuristic approaches. 48V mild hybrid
powertrains with an electrified air path are characterized
by a strong interaction between the powertrain and the
electrical system. This has significant impact on the
degrees of freedom and the complexity of powertrain
management. In addition, increasing 48V electrification in
the various vehicle domains as well as limited electrical
energy and power are further reasons for the importance of
an intelligent energy and power management, which makes the
best possible use of the limited resources of cost
efficiently designed 48V systems. The model predictive
powertrain management developed in this work enables an
optimization-based control of the belt starter generator as
well as the electrified air path via the actuators of the
throttle valve, the waste gate and the electric
supercharger. It is based on a nonlinear model predictive
control (NMPC), which optimizes the drive torque and energy
consumption taking into account the battery state of charge.
The focus of the work is the conception, development and
simulative investigation of the optimization-based control
concept. The investigations concentrate on the one hand on
the analysis of the controller behaviour in exemplary
driving situations and on the other hand on the evaluation
of the response behaviour and fuel consumption in dynamic
driving cycle simulations. The implemented NMPC is based on
a nonlinear differential algebraic equation system to
describe the system dynamics. The continuous time optimal
control problem is discretized through multiple shooting and
solved by sequential quadratic programming (SQP) with a
generalized Gauss-Newton method. The implementation is done
via the MATLAB-based toolkit ACADO (Automatic Control And
Dynamic Optimization). With a discretization time of 40 ms
and a prediction horizon of 720 ms the NMPC can be
implemented in real-time on the PC in combination with a
limitation of the SQP iterations. The controller is able to
robustly control the powertrain’s degrees of freedom over
the entire operating range, even under the influence of high
disturbances. Furthermore, it enables a targeted and fuel
saving use of the 48V system without negatively influencing
the driving dynamics. Under ideal conditions, the presented
NMPC can achieve fuel savings of up to $10.3\%$ in a real
world driving cycle compared to a state-of-the-art rule
based powertrain management. In principle, the potential
increases with increasing knowledge about the future driving
demand and decreasing driver influence. The weighting of the
NMPC allows a calibration between efficient and dynamic
driving behaviour. Overall, the NMPC powertrain management
represents a promising method of effectively controlling
hybrid powertrains with an electrified air path with regard
to driving dynamics and fuel consumption. Since, in contrast
to heuristic methods, it does not require application and
situation specific sets of rules, the approach can be
transferred to similar powertrain concepts and is thus
suitable for reducing the development, adaptation and
calibration effort in the future.},
cin = {422320},
ddc = {620},
cid = {$I:(DE-82)422320_20210420$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2021-09813},
url = {https://publications.rwth-aachen.de/record/834347},
}
guest ::