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Design Optimization Methods for Electrical Drive Systems

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Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems

Part of the book series: Power Systems ((POWSYS))

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Abstract

Electrical drive systems are key components in modern appliances, industry equipment and systems, such as digital machine tools and hybrid and pure electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized simultaneously at the system level rather than the component level. This chapter presents system-level design and optimization methods for electrical drive systems, namely the single-level optimization method, multi-level optimization method, and multi-level Genetic Algorithm (MLGA). Two electrical drive systems are investigated to illustrate the effectiveness of those proposed methods. The performances of two machines are evaluated by the finite element models, which have been verified by comparing with the experimental results on prototypes. The proposed multi-level method can increase the performance of the whole drive system, such as higher output power, lower material cost and lower dynamic overshoot, and decrease the computational cost significantly compared with those of single-level design optimization method.

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Authors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Gang Lei, Jianguo Zhu & Youguang Guo

Authors
  1. Gang Lei
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  2. Jianguo Zhu
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  3. Youguang Guo
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Corresponding author

Correspondence to Gang Lei .

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© 2016 Springer-Verlag Berlin Heidelberg

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Lei, G., Zhu, J., Guo, Y. (2016). Design Optimization Methods for Electrical Drive Systems. In: Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems. Power Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49271-0_5

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  • DOI: https://doi.org/10.1007/978-3-662-49271-0_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49269-7

  • Online ISBN: 978-3-662-49271-0

  • eBook Packages: EnergyEnergy (R0)

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