Traction Electric Motor Design With Reduced-Order Simulation
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Solution Overview
Problem
Designing axial flux permanent magnet synchronous motors (AFPSMs) is challenging due to their geometric complexity and the need for computationally expensive physics-based simulation models, leading to longer simulation times and increased development costs.
Innovation Solution
A multi-stage optimization process utilizing a hardware computing device to execute a global design search, identify high-performing design regions, and employ higher resolution simulations to determine an optimized design, incorporating Design of Experiment (DoE) methods and reduced-order dq models to accelerate the design process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based simulation models (FEA) are used to design axial flux motors, then design accuracy is improved, but simulation time and computational cost increase exponentially
Solution Approach 1:
The patent creates simplified surrogate models that copy the essential behavior of complex FEA simulations. These surrogate models use reduced-order physics-based equations to replicate motor performance characteristics without requiring full 3D FEA analysis, thereby maintaining design accuracy while dramatically reducing computational time and resources.
Solution Approach 2:
The patent transforms the simulation approach by changing from full 3D FEA parameters to reduced-order 2D simulation parameters with explicit mathematical functions. This parameter transformation allows the system to maintain sufficient design accuracy while reducing computational complexity and simulation time from exponential to polynomial scaling.
2Measurement precision
If 3D simulation environment is used for AFPSMs, then design accuracy is improved, but computational speed deteriorates
Solution Approach 1:
The patent creates simplified surrogate models that copy the essential behavior of complex FEA simulations. These surrogate models use reduced-order physics-based equations to replicate motor performance characteristics without requiring full 3D FEA analysis, thereby maintaining design accuracy while dramatically reducing computational time and resources.
Solution Approach 2:
The patent reduces the simulation from three-dimensional to two-dimensional analysis by identifying and eliminating redundant spatial dimensions. This dimensionality reduction allows explicit mathematical functions to be derived that maintain design accuracy while enabling computational speedup from exponential to polynomial complexity scaling.
3Adaptability or versatility
If explicit mathematical functions are not available, then design flexibility is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent transforms the simulation approach by changing from full 3D FEA parameters to reduced-order 2D simulation parameters with explicit mathematical functions. This parameter transformation allows the system to maintain sufficient design accuracy while reducing computational complexity and simulation time from exponential to polynomial scaling.
4Reliability
If geometric complexity of axial flux motors is considered, then design performance is improved, but device complexity increases
Solution Approach 1:
The patent creates simplified surrogate models that copy the essential behavior of complex FEA simulations. These surrogate models use reduced-order physics-based equations to replicate motor performance characteristics without requiring full 3D FEA analysis, thereby maintaining design accuracy while dramatically reducing computational time and resources.
Data Source
AI summary
A method for optimizing a design of an electric motor is disclosed. The method includes receiving, at a hardware computing device, user parameters from a user interface in communication with the hardware computing device. The user parameters include one or more traction electric motor design limitations. The method also includes determining, at the hardware computing device, a problem specification based on the user parameters, and executing, at the hardware computing device, a global design search of traction electric motor designs based on the problem specification within a global design region. The method also includes identifying, at the hardware computing device, a high-performing design region being a portion of the global design region, where the high-performing design region includes multiple motor designs.