Evidence map›Paper›PMID 38443425›Full record

ArticleScientific reports2024

Optimization design of brushless DC motor based on improved JAYA algorithm.

Yuan Cheng, Xueli Lyu, Shasha Mao

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Yuan ChengSchool of Control Engineering, Wuxi Institute of Technology, Wuxi, 214121, China.
Xueli LyuSchool of Control Engineering, Wuxi Institute of Technology, Wuxi, 214121, China. 13921521527@163.com.
Shasha MaoSchool of Control Engineering, Wuxi Institute of Technology, Wuxi, 214121, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5-12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm

Indexed as

Brushless DC motorJAYA algorithmMathematical modelOptimization problemsParameter optimization

Identifiers

PMID38443425
PMCPMC11316109

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