Monumo Limited

Kevin Bersch

Jaroslaw Pawel Rzepecki

Ian Murphy

Markus Kaiser

Daniel Bates

Nicolas Durrande

William Gallafent

Rupert Tombs

Alexey Kostin

Jonathan Rayner

Chun-Ting Lau

Bhaskar Sen

Pierre Guern

Xiaoyan Wang

A switched reluctance electrical motor comprises a rotor having a substantially circular cylindrical envelope with a diameter in a range of 50 mm to 300 mm and a length in the range of 20 mm to 250 mm. The rotor comprises a plurality of radially extending rotor teeth. A switched reluctance electrical motor further comprises a stator surrounding the rotor and comprising a plurality of stator poles. The rotor teeth are circumferentially-spaced apart from each other to define slots between adjacent teeth that, expressed in normalised angular and radial coordinates, have a cross-sectional profile transverse to an axis of rotation of the rotor, lying within a well-defined polygonal region.

Rotor for switch reluctance electrical motor with teeth having irregular cross-sectional profile

A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit is provided. The electric drive unit comprises at least an electric motor. The method comprises obtaining parameters defining physical properties of the electric motor, obtaining parameters defining drive currents for driving the electric motor, processing the obtained parameters using a machine learning module trained a priori to predict a spatially varying electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation, and providing as output a predicted profile for the electric motor, and using the predicted profile to compute the one or more performance parameters of the electric drive unit.

Electric drive unit simulation

A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit is provided. The electric drive unit includes at least an electric motor. The method includes obtaining parameters defining physical properties of the electric motor, obtaining parameters defining drive currents for driving the electric motor, processing the obtained parameters using a machine learning module trained a priori to predict a spatially varying electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation, and providing as output a predicted profile for the electric motor, and using the predicted profile to compute the one or more performance parameters of the electric drive unit.

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Saffron Walden, United Kingdom

Kevin Bersch