@article{LeiseEsserEichenlaubetal.2021,
  author    = {Leise, Philipp and Eßer, Arved and Eichenlaub, Tobias and Schleiffer, Jean-Eric and Altherr, Lena and Rinderknecht, Stephan and Pelz, Peter F.},
  title     = {Sustainable system design of electric powertrains - comparison of optimization methods},
  series = {Engineering Optimization},
  journal   = {Engineering Optimization},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0305-215X},
  doi       = {10.1080/0305215X.2021.1928660},
  year      = {2021},
  abstract  = {The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal 'climate action' stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.},
  language  = {en}
}
@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}
}
@inproceedings{LeiseSimonAltherr2020,
  author    = {Leise, Philipp and Simon, Nicolai and Altherr, Lena},
  title     = {Comparison of Piecewise Linearization Techniques to Model Electric Motor Efficiency Maps: A Computational Study},
  series = {Operations Research Proceedings 2019},
  booktitle = {Operations Research Proceedings 2019},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-48439-2},
  doi       = {10.1007/978-3-030-48439-2_55},
  pages     = {457 -- 463},
  year      = {2020},
  abstract  = {To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.},
  language  = {en}
}