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@PHDTHESIS{Chen:1021037,
      author       = {Chen, Bicheng},
      othercontributors = {Pischinger, Stefan and Andert, Jakob Lukas},
      title        = {{T}hermal behavior impact on electric motor sizing in
                      battery electric vehicles},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-09415},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {The increasing popularity of electric vehicles (EVs) as
                      eco-friendly alternatives in the automotive industry has
                      been impeded by consumer apprehensions regarding limited
                      driving range. This work addresses this challenge by
                      focusing on two pivotal factors crucial for enhancing EV
                      efficiency and extending driving range: accurate temperature
                      monitoring and optimal sizing for electrical machines (EMs).
                      In response to the critical need for monitoring temperatures
                      in electric drivetrain components, a centralized compact
                      lumped parameter thermal network (LPTN) model is proposed.
                      Departing from conventional distributed thermal models for
                      each component, this thermal model considers the intricate
                      thermal coupling between the inverter, EM and gearbox.
                      Utilizing the measured and validated loss maps including the
                      detailed losses distribution in the permanent magnet
                      synchronous machine (PMSM), the model accurately calculates
                      component losses. A global linear parameter-varying (LPV)
                      identification approach is then applied to determine the
                      parameters of the LPTN model. Cross-validation with
                      independent experimental data of the US06 cycle on the
                      chassis dynamometer yields a maximum estimation error of
                      approximately 7 ◦C. Simulation results demonstrate the
                      effectiveness of the centralized thermal model in estimating
                      temperatures of critical parts while considering the thermal
                      coupling between components. Additionally, this work
                      introduces a promising approach known as "right-sizing" for
                      EMs. This involves an efficient scaling of thermal
                      parameters in a low-order LPTN model, enabling the
                      estimation of temperature for the scaled PMSM. The proposed
                      scaling approach facilitates a preliminary evaluation of the
                      thermal limits of the PMSM during the early stages of
                      development. Validation for both axial and radial scaling,
                      with scaling factors ranging from 0.8 to 1.2, is conducted
                      on a previously validated Ansys Motor-CAD model for typical
                      automotive driving cycles, revealing a maximum temperature
                      scaling error of less than 3.5 ◦C. The integration of the
                      LPTN model and the scaling approach into a whole vehicle
                      simulation model becomes instrumental in determining the
                      optimal size of a specific EM for diverse driving scenarios,
                      including urban and highway conditions. The pursuit of
                      optimization is guided by considering critical factors such
                      as the thermal constraints of the EM, the overall efficiency
                      and performance of the EV. Employing the ant colony
                      optimization (ACO) optimization algorithm results in the
                      identification of a Pareto front for urban and highway
                      scenarios. The optimization results suggest that a shorter
                      motor length is advantageous in both urban and highway cycle
                      scenarios. In urban scenarios, the optimized motor enhances
                      acceleration performance while lowering energy consumption.
                      However, in highway scenarios, there’s a trade-off between
                      energy consumption and acceleration, with the optimized
                      motor leading to a $2.84\%$ decrease in energy consumption.},
      cin          = {412310},
      ddc          = {620},
      cid          = {$I:(DE-82)412310_20140620$},
      pnm          = {CEVOLVER - Connected Electric Vehicle Optimized for Life,
                      Value, Efficiency and Range (824295)},
      pid          = {G:(EU-Grant)824295},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-09415},
      url          = {https://publications.rwth-aachen.de/record/1021037},
}