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This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

One of the primary design requirements of automotive generation systems is maximizing power density subject to the constraint of minimizing the overall system cost. However, with the progress made in the reduction of automotive drive train noise, the torque ripple of the generator has been found to be a dominant noise source under idle conditions at high electrical loads. Thus, an added design constraint is the minimization of the torque ripple produced by the machine. In order to evaluate alternative machine designs (and select an optimal), numerical tools are typically applied. In this research, a focus is placed on the creation of numerical tools that can be used to effectively search for an optimal design. A primary tool is an evolutionary algorithm (EA) that has been integrated within a customized magnetic equivalent circuit (MEC) model of the machine. The selection of an EA that is most likely to converge to an optimal solution in the least amount of time is described along with its use in selecting an optimal rotor-pole geometry. [ABSTRACT FROM AUTHOR]