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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]

Energy constraints have a significant impact on the design and operation of wireless sensor networks. This paper investigates the base station (BS) selection (or anycast) problem in wireless sensor networks. A wireless sensor network having multiple BSs (data sink nodes) is considered. Each source node must send all its locally generated data to only one of the BSs. To maximize network lifetime, it is essential to optimally match each source node to a particular BS and find an optimal routing solution. A polynomial time heuristic is proposed for optimal BS selection and anycast via a sequential fixing procedure. Through extensive simulation results, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) are considered to be one of the energy-efficient technologies for near-term sustainability of the transportation sector. Over the years, research has focused on improving fuel economy (FE) for a given drive cycle, but FE variability over a realistic range of real-world driving patterns has been generally overlooked, and this can lead to FE benefits not being fully realized in real-world usage. No systematic methodology exists to reduce FE variability by design optimization of powertrain components. This study proposes a methodology of powertrain component optimization to reduce the FE variability due to variations in driving patterns. In the proposed methodology, powertrain components are optimum over a range of driving patterns of different traffic conditions and driving styles simultaneously. The proposed methodology demonstrates the potential to reduce FE variability by up to 34% over six driving patterns of different traffic conditions and driving styles. [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]