Finding Global-Optimal Gearbox Designs for Battery Electric Vehicles

  • Philipp LeiseEmail author
  • Lena C. Altherr
  • Nicolai Simon
  • Peter F. Pelz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


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.


Powertrain Gearbox Optimization BEV WLTP MINLP Gaussian mixture model Piecewise linearization 



Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 57157498 – SFB 805.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Philipp Leise
    • 1
    Email author
  • Lena C. Altherr
    • 1
  • Nicolai Simon
    • 1
  • Peter F. Pelz
    • 1
  1. 1.Chair of Fluid SystemsTU DarmstadtDarmstadtGermany

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