Optimal Design of PMa-SynRM for Electric Bicycle Traction Motor Using Adjustive-resolution Robustness Optimization Algorithm

Abstract

This paper proposes adjustive-resolution robustness optimization algorithm (AROA) for robust optimal design of electric bicycle (EB) traction motor. Target motor is permanent magnet assisted synchronous reluctance motor and design objectives are average torque, torque ripple, cogging torque, and total harmonic distortion of the line-to-line back electromotive force. The AROA can consider various objectives at the same time using the weighted sum method, and the designer can assign the desired weight according to the model. The proposed algorithm can drastically reduce the number of function calls by interpolating problem regions. Additionally, the AROA proposes novel initial sampling strategy, diversification strategy, intensive searching strategy to effectively adjust the number of generated samples. At the end of the algorithm, robustness test is conducted, and robust optimal solution is determined considering the accuracy of the interpolated uncertainty band of both global and local solutions. The superior performance of the AROA was verified at two mathematical test functions, and the applicability of the practical motor design was verified by applying the AROA to the optimal design of the EB traction motor and successfully deriving the robust optimum design. The accuracy of the analysis was confirmed by manufacturing the prototype motor and comparing the experimental results.