Gear ratio and traction machine optimization strategy for electric vehicles

  • Forschungsthema:Machine design and construction
  • Typ:Bachelor/Master
  • Datum:By agreement
  • Betreuung:

    Erling Gjeset

    Tobias Zeller

  • Bild:

  • Bearbeiter:

    To be assigned

  • Motivation
    When designing electric machines for vehicle traction applications, initial assumptions such as drive train arrangement, machine requirements, size/weight limitations, etc. have a considerable impact on the initial machine configuration and geometric parameters. This, in turn, will define the optimization space for the machine to be constructed.
    The presence and behaviour of the gearbox is one of these deciding factors, and within the study of more lightweight and efficient electrical machines, the gearbox proves to be vital in enabling improved designs by e.g.:

    - Increasing machine rotational speed

    - Decreasing maximum torque requirement

    - Improving dynamic response time

    - Improving drive cycle efficiency and lowering energy consumption

    As such, a proper understanding of the drive train in which the electrical machine is located will greatly improve efficient and lightweight design.

    In this project, the student is tasked with developing an analytical algorithm to pre-define geometric parameters of an electric machine + gearbox for vehicle traction application. The code should be able to read drive cycle and vehicle data and output a list of potential drive train configurations, preferably ranked by a qualitative comparison of, e.g., size, complexity, cost, etc., and/or with remarks concerning potential opportunities or risk factors for the specific case. The output should also provide the defining parameters for the following design and optimization of the electrical machine.

    The work should be structured as follows:
    - Basic theory and literature review
    - Development of the early stage machine + gearbox design algorithm
    - Analysis and evaluation of different machine + gearbox combinations, identifying sensitivity, trends and advantages/drawbacks
    - Implementing the early stage design algorithm into the ETI MagnetA toolchain (Optional)

    Note: Work and supervision will be done in English.