Implementation of synchronous motor optimization based on genetic algorithm in MATLAB

In this study, a genetic algorithm-based design optimization methodology for a permanent magnet synchronous motor (PMSM) is developed to improve energy efficiency and reduce torque pulsation.

The objective of the study was to determine the optimal motor geometric parameters, including magnet wrap angle, magnet thickness, stator tooth width, slot depth, and air gap, taking into account technological constraints and electromagnetic characteristics.

The methodology is based on a combination of analytical modeling of magnetic circuits and a genetic algorithm implemented in MATLAB with a multi-objective fitness function that takes into account torque, pulsation, and efficiency.

The results demonstrate that the proposed approach achieves significant performance improvements: an 8.9% increase in torque, a 40% decrease in pulsation, and a 3.5 percentage point increase in efficiency compared to the baseline configuration. It was found that the optimal configuration is achieved with a magnet coverage angle of 72° and an air gap of 0.85 mm, which confirms the need to use modern optimization methods to find non-trivial technical solutions.

Conclusion. The results obtained are of practical importance for designing energyefficient motors, reducing development time. The study contributes to the development of computer-aided design methods for electrical machines, demonstrating the effectiveness of genetic algorithms for solving complex multi-criteria problems of electromechanics.

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