Component sizing of a plug-in hybrid electric vehicle powertrain, Part A: coupling bio-inspired techniques to meshless variable-fidelity surrogate models

  • Authors:

  • Ahmad Mozaffari;Maryyeh Chehresaz;Nasser L. Azad

  • Affiliations:

  • Systems Design Engineering Department, University of Waterloo, N2L 3G1, Ontario, Canada;Systems Design Engineering Department, University of Waterloo, N2L 3G1, Ontario, Canada;Systems Design Engineering Department, University of Waterloo, N2L 3G1, Ontario, Canada

  • Venue:
  • International Journal of Bio-Inspired Computation
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the present investigation, the authors propose a variable fidelity optimisation framework for component sizing of a plug-in hybrid electric vehicle PHEV powertrain. The proposed computational framework can be divided into two different stages. At the first stage, finite element grids of different resolutions are used to capture initial information regarding the behaviour of physical system. To generate those grids, maximum power of electric motor PEM-max and maximum power of combustion engine PCE-max are fed to a specialised physical model. Based on a cumbersome computational procedure, the physical model yields fuel consumption FC required for a predefined drive cycle. Having such information available, the authors take the advantages of an efficient design of experiment DoE scheme to extract some samples from the generated grids. Thereafter, two surrogate techniques, i.e., respond surface method RSM and radial basis function RBF, are used to approximate the general behaviour of both high fidelity and low fidelity models. At the second stage, the developed surrogate models are used for optimisation. To do so, a recent spotlighted memetic algorithm called scale factor local search differential evolution SFLSDE is used. Through a throughout comparative analysis, the authors prove the proposed model is really effective for PHEV optimisation.