Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

In this paper, circular area movement optimization (CAMO), a novel global search algorithm, and its hybridization with the pattern search method (PSM) are proposed to solve the multimodal optimization problem. The CAMO is an optimization technique that creates a circular search area, moves the area, and searches the entire search area. Depending on the type of samples inside the area, two strategies are used to quickly and efficiently find the optimal point across the area. Also, the hybridization with the PSM supports fast convergence on adjacent optima from the points that are discovered in the global search using the CAMO. The effectiveness of the algorithm was verified by applying the CAMO to two test functions, and its superiority was confirmed through comparison with existing optimization algorithms. In addition, by applying the proposed algorithm to the optimal design of the cogging torque of an interior permanent magnet synchronous motor for a hybrid electric vehicle, a design that reduces the cogging torque by 95.65% was successfully derived. Lastly, stress analysis and demagnetization analysis were performed to examine the structural and thermal stability of the motor.