Motor Parameter Search Engine for Multi-Objective Optimization
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Solution Overview
Problem
In motor design, the extensive physical parameters require significant human resources and time for adjustment across different fields, and there is no unified motor design theory to facilitate multiple or new topology designs, limiting the ability to optimize for multiple objectives simultaneously.
Innovation Solution
A motor parameter search system and method that utilize a processing device with modules for parameter search, simulation, and optimal combination recommendation, allowing for iterative generation and optimization of motor design parameters based on received design parameters, optimization objectives, and restriction conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual parameter adjustment methods are used, then designers can adjust motor parameters, but significant human resources and time are consumed
Solution Approach 1:
The system enables self-service through automated parameter search and optimization. The motor design parameter search engine automatically generates design parameter combinations and iteratively searches for optimal parameters based on optimization objectives and restriction conditions, eliminating the need for manual back-and-forth adjustment between different physical fields by designers.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated computer-based system. The motor design parameter search engine substitutes human designers' manual parameter adjustment with automated iterative searching and simulation, significantly reducing time consumption and human resource requirements.
2Adaptability or versatility
If traditional motor design methods are used, then designers can adjust parameters, but there is no unified motor design theory to facilitate multiple or new topology designs
Solution Approach 1:
The system achieves universality through a unified motor design theory framework that can handle multiple motor topologies and design scenarios. The parameter search engine and optimization module are designed to be topology-agnostic, allowing the same system to optimize parameters for different motor types (e.g., PMSM, induction motors) without requiring separate design methodologies.
Solution Approach 2:
The system incorporates dynamic adaptability by allowing the optimization objectives and restriction conditions to be flexibly configured for different design scenarios. The iterative parameter search can adapt to various topology requirements by adjusting the design parameters, optimization objectives, and constraints, making the system versatile across multiple design contexts.
3Productivity
If traditional design methods are used, then parameters can be adjusted, but optimization for multiple objectives cannot be performed simultaneously
Solution Approach 1:
The system merges multiple optimization objectives into a unified optimization framework. The motor design parameter search engine simultaneously handles multiple optimization objectives (e.g., efficiency, power density, cost) and restriction conditions by integrating them into a single iterative parameter search process, allowing concurrent optimization of multiple objectives rather than sequential single-objective optimization.
Data Source
AI summary
A motor parameter search system and a motor parameter search method are provided. The motor parameter search system includes a processing device and an input device. The processing device may execute a motor design parameter search engine to iteratively perform a parameter search operation based on a plurality of design parameters, a plurality of optimization objectives, a plurality of restriction conditions, and a plurality of historical recommended parameter combinations to generate a plurality of design parameter combinations. The design parameter combinations are sequentially input into a simulation software, so that the simulation software generates a plurality of simulation results. The processing device searches a plurality of historical simulation results in the database according to the plurality of optimization objectives to generate a recommended parameter combination.