Robust multi-objective transverse flux machine drive optimization considering torque ripple and manufacturing uncertainties

This paper presents a multi-objective robust optimization method for a drive system consisting of a permanent magnet transverse flux machine with soft magnetic composite cores and a field-oriented controller. Unlike existing research work, the torque ripple is considered an optimization objective. Several machine uncertainties caused by manufacturing tolerances are investigated in the robust optimization model under the framework of the design for six-sigma. Since this is a system-level optimization problem, two approximation models are employed to decrease the computational cost. First, a Kriging model is used to approximate the steady-state electromagnetic performances of the motor, such as the output power and efficiency. Second, a Taylor series approximation is employed to estimate the dynamic performances of the control system, such as the speed overshoot and settling time. Furthermore, a sampling selection method is proposed to reduce the computational cost of the Monte Carlo analysis in robust optimization. To show the effectiveness of the proposed method, both deterministic and robust Pareto solutions are presented and discussed. It can be seen that the system-level multi-objective design optimization based on robust approach can produce optimal Pareto solutions with a high manufacturing quality for the whole drive system. This is valuable for the batch production of electrical drive systems.