Particle Swarm Optimization of coupled electromechanical systems

Al-Aawar,; Hijazi,; Arkadan,

doi:10.1109/cefc.2010.5481415

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…The EM module is used to compute the WAD inductance family of curves for the five trail particles of each of the 18 categories, for a total of 90 solutions. The results of the state space model of the EM module, which include the output current, are used to train an adaptive neuro fuzzy inference system (ANFIS) for a category [6].…”

Section: A Taguchi Oa Methodsmentioning

confidence: 99%

“…In addition, it divides each range into three equal intervals or levels designated as: 1) 1 for minimum; 2) 2 for medium; and 3) 3 for maximum. In this paper, the following ranges are used for the parameters: 1) P1 [4][5][6][7][8][9][10][11][12]; 2) P2 [3][4][5][6] The Taguchi array used in this paper consists of 18 combinations or categories [7], as shown in Table I. In addition, five trial particles or design vector values are used for a category.…”

Section: A Taguchi Oa Methodsmentioning

confidence: 99%

“…The design optimization environment implemented in this paper employs a PSO algorithm [6] that requires a large number of accurate design evaluations due to the stochastic nature of the water wave and the nonlinear dynamic response of the WAD. Hence, a statistical method based on Taguchi OAs is used to reduce the needed computational time [7].…”

Section: Pso-em-taguchi Algorithm and Applicationmentioning

confidence: 99%

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EM-Taguchi Module for Characterization of WAD

2015

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Accurate performance characterization is critical in a design optimization environment. The electromagnetic (EM) design optimization of an ocean wave linear generator buoy or wave activated device (WAD) requires the use of large number of finite element-state space, design evaluations due to the stochastic behavior of the wave and the nonlinear nature of the device. This paper proposes the utilization of Taguchi orthogonal arrays method to reduce the EM, computational time needed to accurately characterize a WAD performance corresponding to a sea wave profile and for use in a design optimization environment.

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Section: A Taguchi Oa Methodsmentioning

confidence: 99%

Section: A Taguchi Oa Methodsmentioning

confidence: 99%

Section: Pso-em-taguchi Algorithm and Applicationmentioning

confidence: 99%

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EM-Taguchi Module for Characterization of WAD

2015

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“…Algorithms used in the global EMSs include dynamic programming, 18 stochastic control 19 and particle swarm optimization. 20 Local EMSs are classified further into rule-based EMSs and optimization-based EMSs. On one hand, the rule-based EMSs mainly rely on the heuristic knowledge of powertrain system, operating state and driving cycle.…”

Section: Introductionmentioning

confidence: 99%

Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses

Nguyen

Walker

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Powertrain configuration plays an important role in the performance of plug-in hybrid electric buses. Current designs are the compromise between energy efficiency, dynamic ability, shifting smoothness and manufactural cost. To balance the above requirements, this research proposes a novel dual motor powertrain for plug-in hybrid electric buses. The efficiency improvement is compared to the conventional plug-in parallel hybrid electric buses with a single motor powertrain. Parameter designs of system components guarantee two configurations equivalently. To maximize the benefits of the proposed powertrain, this paper introduces an energy management strategy which coordinates enumeration method and dynamic programming to build the optimal maps of powertrain operation. The enumeration method determines the working points of power sources and gear states in all possible modes according to vehicle speed and power. The dynamic programming then selects the most suitable mode with the consideration of gear shifting and mode change in the optimal maps. Simulation results show that the dual motors work in peak efficiency region much more frequently than the single motor in different conditions. Therefore, the total energy cost of dual motor powertrain for entire driving cycles decreases significantly in comparison with the single motor powertrain, 6.5% in the LA92 and 6.7% in the Urban Dynamometer Driving Schedule.

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“…In this sense, many approaches have been applied for the automatic generation of fuzzy systems from data. Neural networks (Aliev et al, 2011), genetic algorithms (Herrera, 2008), and, more recently, other bio-inspired approaches, such as particle swarm optimization (Al-Aawar et al, 2011;Pousinho et al, 2011), ant colony optimization (Ahmadizar and Soltanpanah, 2011), and artificial immunological systems (Prakash and Deshmukh, 2011) are among the most successful techniques for the automatic generation of fuzzy systems.…”

Section: Motivationmentioning

confidence: 99%

Genetic generation of fuzzy knowledge bases: new perspectives

Cintra¹

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Particle Swarm Optimization of coupled electromechanical systems

Cited by 3 publications

References 2 publications

EM-Taguchi Module for Characterization of WAD

EM-Taguchi Module for Characterization of WAD

Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses

Genetic generation of fuzzy knowledge bases: new perspectives

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