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A large-scale finite element model-based design optimization algorithm is developed for improving the drive-cycle efficiency of permanent magnet (PM) synchronous machines with wide operating ranges such as those used in traction propulsion motors. The load operating cycle is efficiently modeled by using a systematic k-means clustering method to identify the operating points representing the high-energy-throughput zones in the torque–speed plane. The machine performance is evaluated over these cyclic representative points using a recently introduced computationally efficient finite element analysis, which is upgraded to include both constant torque and field-weakening operations in the evaluation of the machine performance metrics. In contrast with the common practice, which aims at enhancing the rated performance, the entire range of operation is considered in the present design optimization method. Practical operational constraints imposed by the voltage and current limits of the motor-drive system, excessive PM demagnetization, and torque ripple are accounted for during the optimization process. The convergence to the optimal design solutions is expedited by utilizing a new stochastic optimizer. The developed design algorithm is applicable to any configuration of sinewave-drive PM and synchronous reluctance motors over any conceivable load profile. Its effectiveness is demonstrated by optimizing the well-established benchmark design represented by the Toyota Prius Gen. 2 interior PM motor configuration over a compound operating cycle consisting of common U.S. driving schedules. Multiphysics electromagnetic, thermal, and mechanical performance of the optimized design solutions is discussed in a postdesign optimization stage. [ABSTRACT FROM PUBLISHER]

Performance improvement of permanent magnet (PM) motors through optimization techniques has been widely investigated in the literature. Oftentimes the practice of design optimization leads to derivation/interpretation of optimal scaling rules of PM motors for a particular loading condition. This paper demonstrates how these derivations vary with respect to the machine ampere loading and ferrous core saturation level. A parallel sensitivity analysis using a second-order response surface methodology followed by a large-scale design optimization based on evolutionary algorithms are pursued in order to establish the variation of the relationships between the main design parameters and the performance characteristics with respect to the ampere loading and magnetic core saturation levels prevalent in the naturally cooled, fan-cooled, and liquid-cooled machines. For this purpose, a finite-element-based platform with a full account of complex geometry, magnetic core nonlinearities, and stator and rotor losses is used. Four main performance metrics including active material cost, power losses, torque ripple, and rotor PM demagnetization are investigated for two generic industrial PM motors with distributed and concentrated windings with subsequent conclusions drawn based on the results. [ABSTRACT FROM PUBLISHER]

Large-scale design optimization of electric machines is oftentimes practiced to achieve a set of objectives, such as the minimization of cost and power loss, under a set of constraints, such as maximum permissible torque ripple. Accordingly, the design optimization of electric machines can be regarded as a constrained optimization problem (COP). Evolutionary algorithms (EAs) used in the design optimization of electric machines including differential evolution (DE), which has received considerable attention during recent years, are unconstrained optimization methods that need additional mechanisms to handle COPs. In this paper, a new optimization algorithm that features combined multi-objective optimization with differential evolution (CMODE) has been developed and implemented in the design optimization of electric machines. A thorough comparison is conducted between the two counterpart optimization algorithms, CMODE and DE, to demonstrate CMODE’s superiority in terms of convergence rate, diversity and high definition of the resulting Pareto fronts, and its more effective constraint handling. More importantly, CMODE requires a lesser number of simultaneous processing units which makes its implementation best suited for state-of-the-art desktop computers reducing the need for high-performance computing systems and associated software licenses. [ABSTRACT FROM PUBLISHER]