[W-OP-96] Necessity of GA for Multi-Objective Optimization of Drive Motor

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1.The need for multi-objective optimization for traction motor design

Tractions motors for HEVs and EVs require different performances depending on the driving conditions and may be restricted not only in terms of magnetic fields performance but also in terms of structure and heat performances. For example, high torque and low torque ripple are required in the low speed / high load driving state, and a low torque ripple and a voltage limitation are imposed in the high speed / low load driving state etc. In this way, as a motor design method that satisfies a plurality of required values, multi-objective optimization calculation may be performed. By setting the required values as an objective function, and constraint conditions, to minimize the objective function, we will be able to search for an optimal design.
Usually, when performing such multi-objective optimization calculations, a sensitivity analysis is performed to clarify design variables contributing to the minimization of the objective function to simplify the search for the optimum solution from a huge design space within a realistic time range. Only then the optimization calculation is performed. On the other hand, even with the result of a sensitivity analysis, it may be difficult to narrow down the design space because all design variables present high sensitivity on the objectives. In such a case, it is thought that it is a reasonable approach to perform a multi-objective optimization calculation using a Genetic Algorithm (hereinafter called “GA”) to find the best solution and make a design proposal. In this paper, a GA optimization calculations will be carried out for a problem where the narrowing down of the design space is difficult based on the results of sensitivity analysis. Furthermore, it was shown that a good compromise to the design solution can be found.

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