US 12,118,280 B1
Electric drive unit simulation
Chun-Ting Lau, Saffron Walden (GB); Daniel Bates, Saffron Walden (GB); Kevin Bersch, Saffron Walden (GB); William Gallafent, Saffron Walden (GB); Jaroslaw Pawel Rzepecki, Saffron Walden (GB); Alexey Kostin, Saffron Walden (GB); Jonathan Rayner, Saffron Walden (GB); Markus Kaiser, Saffron Walden (GB); Nicolas Durrande, Saffron Walden (GB); Rupert Tombs, Saffron Walden (GB); and Ian Murphy, Saffron Walden (GB)
Assigned to Monumo Limited, Essex (GB)
Filed by Monumo Limited, Saffron Walden (GB)
Filed on Apr. 3, 2024, as Appl. No. 18/625,435.
Application 18/625,435 is a continuation of application No. 18/447,791, filed on Aug. 10, 2023, granted, now 11,977,826.
Int. Cl. G06F 30/27 (2020.01); G06F 30/15 (2020.01); G06F 119/18 (2020.01)
CPC G06F 30/27 (2020.01) [G06F 30/15 (2020.01); G06F 2119/18 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit, the electric drive unit comprising at least an electric motor, the method comprising:
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 a parameter indicating an uncertainty associated with the predicted profile,
determining that the uncertainty associated with the predicted profile is greater than a threshold;
generating, in response to determining that the uncertainty associated with the predicted profile is greater than the threshold, a new predicted profile comprising a training electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation generated by solving a system of predefined equations for the obtained parameters;
performing additional training of the machine learning module using the new predicted profile and the obtained parameters; and
using the new predicted profile to compute the one or more performance parameters of the electric drive unit.