This article proposes a novel design strategy for a PMBLDC motor through hybrid optimization, combining Cauchy Particle Swarm Optimization and Moth Flame Optimization approaches to find the optimal set of design parameters that yield the highest efficiency. The motivation is to improve the efficiency of PMBLDC motors, which are widely used in electric cars and renewable energy systems that place a premium on minimal power usage. The proposed work implements the hybrid optimization technique performs conventional single optimization strategies in terms of efficiency improvement. This study contributes in identifying the optimal values for the various design parameters of PMBLDC motors using the hybrid CPSO-MFO approach, which converged to the optimal design parameters. The performance of the PMBLDC motor was evaluated using the optimal design parameters, with an overall power loss of 49.1 W, and an efficiency of 97.88%. The study's results provide valuable insights for design engineers into the optimal geometrical parameters of PMBLDC motors to achieve higher efficiency and lower power loss. The proposed novel hybrid optimization approach, which is faster and more efficient than conventional single optimization approaches, making it a useful tool for designing high-performance PMBLDC motors.
机构:
Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Minist Educ, Key Lab Complex Syst Intelligent Control & Decis, Beijing 100081, Peoples R ChinaBeijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Wang, Guanghui
Chen, Jie
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Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Minist Educ, Key Lab Complex Syst Intelligent Control & Decis, Beijing 100081, Peoples R ChinaBeijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Chen, Jie
Cai, Tao
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机构:
Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Minist Educ, Key Lab Complex Syst Intelligent Control & Decis, Beijing 100081, Peoples R ChinaBeijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Cai, Tao
Xin, Bin
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机构:
Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
Minist Educ, Key Lab Complex Syst Intelligent Control & Decis, Beijing 100081, Peoples R China
Univ Manchester, Decis & Cognit Sci Res Ctr, Manchester Business Sch, Manchester M15 6PB, Lancs, EnglandBeijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China