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Use of reluctance network modelling and software component to study the influence of electrical machine pole number on hybrid electric vehicle global optimization

Author

Listed:
  • Guyadec, M. Le
  • Gerbaud, L.
  • Vinot, E.
  • Reinbold, V.
  • Dumont, C.

Abstract

In the paper, the global optimization of hybrid electric vehicle (HEV) components and control is performed using genetic algorithm and dynamic programming. Reluctance network modelling (RNM) is used to describe the behaviour of the electrical machine (EM). The pole number is considered as a design variable in the EM model. A software component is built from this model and is used in Matlab for a sizing by optimization. The influence of the EM pole number on the system optimization is analysed. Contrary to the low differences observed on the energy efficiency of the vehicle, the machine shape is highly impacted.

Suggested Citation

  • Guyadec, M. Le & Gerbaud, L. & Vinot, E. & Reinbold, V. & Dumont, C., 2019. "Use of reluctance network modelling and software component to study the influence of electrical machine pole number on hybrid electric vehicle global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 79-90.
    • Handle: RePEc:eee:matcom:v:158:y:2019:i:c:p:79-90
    DOI: 10.1016/j.matcom.2018.06.001
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    References listed on IDEAS

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    1. Yuan Zou & Fengchun Sun & Xiaosong Hu & Lino Guzzella & Huei Peng, 2012. "Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle," Energies, MDPI, vol. 5(11), pages 1-14, November.
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