The uncertainty of electric machines and drives is inherent in its manufacturing and assembly. Robust design optimization finds the design under these uncertainties that results in a minimal variance while satisfying all design constraints. To obtain an accurate result, the statistical model is significantly important. Moreover, several uncertainties from a single source can be involved owing to multiple components in the electrical machines and drives. However, this can drastically increase the numerical cost for the conventional robust design optimization technique. In this work an uncertainty characterization method is developed by using a percentile bootstrap interval to consider the experimental results from few prototypes and a kriging surrogate model to reduce the computational cost. Then the sample-based robust design optimization is applied to the surface-mounted permanent magnet synchronous motor. It is seen that the developed methods can efficiently work to minimize both the mean and variance of the cogging torque while satisfying other design constraints.

Although many routing algorithms have been developed, it is difficult for designers in the automotive industry to adopt them because of the complicated preliminary steps that are required. This study presents a systematic framework for generating the routing layout of the tubes, hoses, and cable harnesses in a commercial truck. The routing layout design problem in a commercial truck is analysed and defined. For routing operations, a sequential graph-based routing algorithm is employed to rapidly provide a routing solution. Because a reference routing layout design does not exist in most engineering problems, a cell-based genetic algorithm combined with a modified maze algorithm is employed to generate a reference design. To consider the clamping condition of the routing components, a new fitness function in the genetic algorithm is implemented. The numerical study shows that the proposed routing algorithm provides a better reference routing layout design than the conventional algorithm. The proposed automatic design system was applied to the routing layout design problem of a commercial truck. It was demonstrated that the proposed framework satisfies all industrial practitioners’ functional requirements and provides a systematic method of solving the routing layout design problem, considering all its characteristics.

The uncertainty of an electromagnetic device is inherent in its manufacturing process. To consider the various uncertainties, several probabilistic design optimisation techniques, such as robust or reliability‐based design optimisation, have been developed. Although a statistical model of uncertainties is extremely important in obtaining an accurate result from a probabilistic design optimisation, most studies on probabilistic design optimisation have assumed these uncertainties to follow normal distributions. However, this assumption may not be valid in several real‐world applications. Therefore, this study presents an efficient uncertainty identification method that provides a systematic framework to select the fittest distribution and find its optimal statistical parameters using finite element analysis and experimental data from prototype testing. The Akaike information criterion and maximum likelihood estimation are used for model selection and parameter estimation, respectively. To reduce the computational cost, the kriging surrogate model is used to evaluate the response of the electromagnetic device. The proposed method is applied to a surface‐mounted permanent magnet synchronous motor, to identify the uncertainties that produce the additional harmonic components of cogging torque. The results show that this method is a powerful tool in analysing the effect of uncertainties on the performance of an electromagnetic device.