Generative adversarial network-based driving motor design method for electric vehicle

A technology for electric vehicles and driving motors, applied in electric vehicles, manufacturing motor generators, motors, etc., can solve the problems of large energy consumption calculations, and achieve the effects of increasing calculation speed, improving accuracy, and self-optimizing agent models

Active Publication Date: 2020-09-01
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

[0005] In view of the shortcomings of the existing situation, the present invention adopts a design method for electric vehicle drive motors based on Generative Adversarial Networks (GAN), which can effectively solve the problem of large amount of energy consumption calculations under cyclic working conditions in calculations, and Realize the performance optimization of drive motors for vehicles in the full speed range

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  • Generative adversarial network-based driving motor design method for electric vehicle
  • Generative adversarial network-based driving motor design method for electric vehicle
  • Generative adversarial network-based driving motor design method for electric vehicle

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Embodiment Construction

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the description of the embodiments or the prior art.

[0061] Below will combine the appended in the embodiment of the present invention figure 1 , fully and clearly describe the technical solution of the present invention.

[0062] The target model of motor design among the present invention is:

[0063]

[0064] s.t.

[0065] x i,min ≤x i ≤x i,max i=1,2,...,n

[0066] J≤J max

[0067] U≤U max

[0068] T Jmax ≥T max

[0069] ω t ≥ω t,min

[0070] where TD nom is the torque density at the nominal operating point, E c is the total energy loss of the motor under the vehicle cycle condition, x is the structural parameter of the motor, n is the number of structural parameters, and J is the current density of the copper core. U is the terminal voltage, which can be an effecti...

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Abstract

The invention provides a generative adversarial network-based driving motor design method for an electric vehicle. The method solves the problem that the dynamic change of a working point of a drivingmotor for the vehicle does not have a typical working point in the driving process, and a generative adversarial network is used as an agent model to replace a finite element to calculate a motor efficiency distribution diagram, so that the energy loss under the automobile driving cycle working condition is further calculated, and the optimization speed under dense working points is increased. Inthe optimization iteration, the finite element simulation results of a new generation of individuals are collected, and the generative adversarial network is continuously retrained, so that the precision of the agent model is self-optimized along with the convergence of an evolutionary algorithm.

Description

technical field [0001] The invention relates to the field of design of drive motors for electric vehicles, in particular to a design method for drive motors for electric vehicles based on generative confrontation networks. Background technique [0002] Motor design is essentially a multi-objective optimization problem. The design of drive motors for vehicles needs to ensure high power density, high efficiency, wide speed range and smooth operation as much as possible under the constraints of volume, heat dissipation and controller. Different from traditional industrial drive motors, the drive motor no longer has a typical rated operating point when driving on pure electricity, and its torque and speed output will be dynamically adjusted as the driving conditions change, which requires that the motor design phase must be considered at the same time Performance optimization under multiple operating points. However, the high nonlinearity of the motor system and the high time-...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02K15/00G06F30/27
CPCG06F30/27H02K15/0006H02K2213/03Y02T10/64
Inventor 花为
Owner SOUTHEAST UNIV
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