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Rapid Electric Motor Sizing Estimation for Automotive Application with Statistical Approach Using Catalog Values

Giovanni Filomeno 1,2, Bastian Krüger 1,2, Peter Tenberge 1, and Dirk Dennin2
1. Industrial and Automotive Drivetrains, Ruhr-University of Bochum, Germany
2. Advanced Development Transmission and Powertrain Simulation, BMW-Group, Munich, Germany

Abstract— This paper presents a rapid sizing method for electrical machines estimating the length, rotor and stator diameter using only the maximum torque, the maximum speed, the maximum power and efficiency as input parameters. The method allows the calculation of separately excited synchronous (SSM), asynchronous (ASM) and permanent synchronous machines (PSM). To calculate the missing parameters needed for the estimation, a pre-compiled database of motors is considered. These parameters can be calculated using statistical interpolation methods. The proposed method combines these statistically calculated parameters with physical relations. Advantages of this method include the possibility to estimate the volume of the electrical motor as well as to use the interpolated data to scale the dimensions of the engine, which enables integrating the motor in a constrained space. To prove the validity of the method, different motors not present in the interpolated database are redesigned with the proposed method. This comparison shows that the model can display a divergence in a range of -6% to +10% in the estimation, which depends on the type of motor. This is small enough to be applied in the automatized design method of automotive transmission. The computational time of 1-2 s per motor design proves that the algorithm operates fast enough. 

Index Terms—Electric motor design, hybrid vehicle, parameter estimation, transmission synthesis

Cite: Giovanni Filomeno, Bastian Krüger, Peter Tenberge, and Dirk Dennin, "Rapid Electric Motor Sizing Estimation for Automotive Application with Statistical Approach Using Catalog Values," International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 11, pp. 1457-1462, November 2020. DOI: 10.18178/ijmerr.9.11.1457-1462

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.