Multi-Objective Optimum Design for in-Wheel Motor Based on Improved Artificial Bee Colony Algorithm

In order to improve the power density of the in-wheel motor and reduce its material cost, an improved artificial bee colony (IABC) algorithm was proposed to optimize the performance of the in-wheel motor. Firstly, the expressions for the performances of the permanent-magnet in-wheel motor with outer rotor were established by magnetic circuit method. Secondly, individual extremum, population extremum and a pair of asynchronous scaling factors were introduced to overcome the shortcomings of traditional artificial bee colony (ABC) algorithm, such as slow convergence speed, and imbalance in exploration and development. The effective mass, power loss and material cost of the motor were linearly weighted to form a single objective function with the electromagnetic parameters such as number of pole pairs, air-gap clearance and permanent magnet thickness as design variables, and the constrained non-linear objective function was transformed into a non-constrained one by the barrier function method. Finally, the genetic algorithm (GA), traditional ABC algorithm and IABC algorithm were used to optimize the design of the in-wheel motor respectively. The correctness of the calculation results was verified by finite element method and prototype experiment. The results show that the IABC algorithm makes the objective function converge faster than the traditional ABC algorithm. Compared with the GA and traditional ABC algorithm, the IABC algorithm minimizes the objective function value. Compared with the original design, the effective quality of the in-wheel motor is reduced by 13.4%, material cost is reduced by 34.4%, power loss is reduced by 44.2%, and the efficiency is increased by 12.0%.