Synthetically optimal design of a direct‐drive surface‐mounted permanent magnet in‐wheel motor

Abstract

In‐wheel motors (IWMs) are one of the most important key technologies to be used by electric vehicles, hybrid electric vehicles, and fuel cell vehicles. To meet the limitations of the space within the wheel rim, unsprung mass, and drive without a multispeed transmission, IWMs need to be designed to simultaneously have high torque per volume and power per mass and wide high‐efficiency operation region. In addition, it is obligatory to meet the requirements of torque output capacity, speed capacity, torque ripple, peak efficiency, power output capacity, etc. In this paper, a direct‐drive surface‐mounted permanent magnet in‐wheel motor (DSPMIWM) is synthetically optimized. Above design demands are accounted for simultaneously by selecting some of them as the design objectives and setting the others as constraints. Firstly, simplified design equations of the DSPMIWM, which can reduce the number of design variables, are established. The size parameters of the DSPMIWM are classified by constants, indirect design parameters, and direct design parameters. The direct design parameters are selected as optimal design variables. Then, a synthetically optimal design of the DSPMIWM is executed by employing the Kriging method combined with the Latin Hypercube sampling and Genetic Algorithm. Compared with an existing mature design, the torque per volume, power per mass, and high‐efficiency operation region of the studied IWM are still improved while all constraints are met. Finally, the effectiveness of the proposed synthetically optimal design procedure is verified by finite element analysis. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Compared with the Monte Carlo method, the Latin hypercube method uses out‐of‐order stratified sampling, which can cover the entire sample space more comprehensively through a small number of sample points [34]. The Kriging model‐fitting method is a regression algorithm based on covariance function for spatial modeling and interpolation, which has been widely used in product structure design of many industries [35,36]. The coefficient of prognosis ( CoP ) is a model quality assessment method independent of the response surface.…”