Abstract
In this paper, internal division point genetic algorithm (IDP-GA) was proposed to lessen the computational burden of multi-variable multi-objective optimization problem using finite element analysis such as optimal design of electric bicycles. The IDP-GA could consider various objectives with normalized weighted sum method and could reduce the number of function calls with novel crossover strategy and vector-based pattern search method. The superiority of the proposed algorithm was verified by comparing performances with conventional optimization method at two mathematical test functions. Finally, the applicability of the IDP-GA in practical electric machine design was verified by successfully deriving an improved design of electric bicycle propulsion motor.
Highlights
Electric bicycles (EBs) are getting more attention in many countries for their convenience, long travelling distance, and environment friendly features [1,2,3]
This paper introduces the internal division point genetic algorithm (IDP-genetic algorithm (GA)) to relieve the huge computational burden of the MVMO optimization problem by applying finite element analysis (FEA)
The IDP-GA accelerated the convergence by applying novel crossover strategy at the early stage of the algorithm
Summary
Electric bicycles (EBs) are getting more attention in many countries for their convenience, long travelling distance, and environment friendly features [1,2,3]. When designing a motor for EBs, high torque density and improving the riding impression through the reduction of noise and vibration are required [3,5]. This paper proposes the novel optimization algorithm that can consider many variables and many objective functions simultaneously with a reduced number of function calls. For the motor design optimization problem, multi-modal optimization algorithm, such as niching GA (NGA), was proposed to find both global and local solutions [12]. A novel optimization algorithm that can consider multiple variables and multiple objectives is required. To verify the applicability of actual electric machines, proposed algorithm is applied to optimal design of PMa-SynRM for EBs and successfully derives design with superior performances
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