@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}
}
@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}
}