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@PHDTHESIS{Ackermann:1017143,
author = {Ackermann, Philipp},
othercontributors = {Mitsos, Alexander and von der Aßen, Niklas Vincenz},
title = {{D}esign of well-to-wheel-optimal alternative fuels},
volume = {39},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-07159},
series = {Aachener Verfahrenstechnik series - AVT.SVT - Process
systems engineering},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {Renewable fuels can enable climate neutral transportation
and require less changes in the infrastructure for storage
and handling than electromobility. However, compared to
battery and fuel cell electric vehicles, renewable fuels
require more conversion steps with associated energy
demands. Hence, the development of renewable fuels should
aim at a strong well-to-wheel performance. An important
lever for well-to-wheel performance is the thermodynamic
efficiency of engines, in particular spark-ignition engines.
The achievable SI engine efficiency depends on the
properties of the fuel, which can be tailored using
optimization-based fuel design. This thesis aims to
integrate the prediction of engine efficiency into fuel
design with the final goal of optimizing the well-to-wheel
performance. First, an optimization-based molecular fuel
design method is built that maximizes spark-ignition engine
efficiency. An empirical model is employed to assess the
achievable engine efficiency for each candidate fuel as a
function of fuel properties. The fuel properties are
predicted automatically using predictive thermodynamics for
phyisco-chemical properties and machine learning models for
combustion properties, indicators for environment, health
and safety, and synthesizability. The method is applied to
design pure-component fuels and binary ethanol-containing
fuel blends. However, due to the simple nature of the
property-based efficiency estimation and uncertainties in
the property prediction, the efficiency predictions are
subject to high uncertainties. To overcome the limitations
of property-based engine efficiency estimation, fuel and
engine are co-optimized simultaneously for selected fuel
molecules. To this end, a lumped dynamic spark-ignition
engine model is developed that predicts the engine
performance as a function of fuel composition and engine
configuration. The engine model is calibrated against
experimental data from a single-cylinder research engine,
such that new candidate fuels require no model recalibration
with additional experimental engine data. As a case study,
10 possible alternative fuel components from previous
studies are selected and used to create binary and ternary
fuel mixtures. The composition of each fuel mixture is then
co-optimized together with the compression ratio and the
intake pressure of the engine considering knock and peak
pressure constraints to ensure smooth and safe engine
operation. The co-optimization reveals the small esters
methyl acetate and ethyl acetate as promising fuel
candidates for future spark ignition engines. Subsequently,
the scope is broadened from tank-to-wheel to well-to-wheel
performance as the target of fuel design. A previously
developed process superstructure-based fuel design method is
combined with the engine model. To include engine efficiency
into the optimization problem, a surrogate model based on an
artificial neural network is derived. Then, alternative
fuels with optimal well-to-wheel performance are designed.
The results show that aiming at a very high engine
efficiency by means of high requirement on the octane number
impairs the well-to-wheel performance. Furthermore,
alternative fuels for tailored engines show more potential
for cost and global warming impact reductions than drop-in
fuels designed with the same superstructure.},
cin = {416710},
ddc = {620},
cid = {$I:(DE-82)416710_20140620$},
pnm = {DFG project G:(GEPRIS)390919832 - EXC 2186: Das Fuel
Science Center – Adaptive Umwandlungssysteme für
erneuerbare Energie- und Kohlenstoffquellen (390919832)},
pid = {G:(GEPRIS)390919832},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2025-07159},
url = {https://publications.rwth-aachen.de/record/1017143},
}
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