@incollection{LeiseAltherrSimonetal.2019, author = {Leise, Philipp and Altherr, Lena and Simon, Nicolai and Pelz, Peter F.}, title = {Finding global-optimal gearbox designs for battery electric vehicles}, series = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, booktitle = {Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-21802-7}, doi = {10.1007/978-3-030-21803-4_91}, pages = {916 -- 925}, year = {2019}, abstract = {In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.}, language = {en} } @incollection{StengerAltherrAbel2019, author = {Stenger, David and Altherr, Lena and Abel, Dirk}, title = {Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead}, series = {Operations Research Proceedings 2018}, booktitle = {Operations Research Proceedings 2018}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-18499-5 (Print)}, doi = {10.1007/978-3-030-18500-8_47}, pages = {379 -- 385}, year = {2019}, abstract = {In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.}, language = {en} } @incollection{PfetschAbeleAltherretal.2021, author = {Pfetsch, Marc E. and Abele, Eberhard and Altherr, Lena and B{\"o}lling, Christian and Br{\"o}tz, Nicolas and Dietrich, Ingo and Gally, Tristan and Geßner, Felix and Groche, Peter and Hoppe, Florian and Kirchner, Eckhard and Kloberdanz, Hermann and Knoll, Maximilian and Kolvenbach, Philip and Kuttich-Meinlschmidt, Anja and Leise, Philipp and Lorenz, Ulf and Matei, Alexander and Molitor, Dirk A. and Niessen, Pia and Pelz, Peter F. and Rexer, Manuel and Schmitt, Andreas and Schmitt, Johann M. and Schulte, Fiona and Ulbrich, Stefan and Weigold, Matthias}, title = {Strategies for mastering uncertainty}, series = {Mastering uncertainty in mechanical engineering}, booktitle = {Mastering uncertainty in mechanical engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78353-2}, doi = {10.1007/978-3-030-78354-9_6}, pages = {365 -- 456}, year = {2021}, abstract = {This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.}, language = {en} } @incollection{AltherrLeise2021, author = {Altherr, Lena and Leise, Philipp}, title = {Resilience as a concept for mastering uncertainty}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78353-2}, doi = {10.1007/978-3-030-78354-9}, pages = {412 -- 417}, year = {2021}, language = {en} } @incollection{AltherrLeisePfetschetal.2021, author = {Altherr, Lena and Leise, Philipp and Pfetsch, Marc E. and Schmitt, Andreas}, title = {Optimal design of resilient technical systems on the example of water supply systems}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {429 -- 433}, year = {2021}, language = {en} } @incollection{LeiseAltherr2021, author = {Leise, Philipp and Altherr, Lena}, title = {Experimental evaluation of resilience metrics in a fluid system}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {442 -- 447}, year = {2021}, language = {en} }