Component sizing of a plug-in hybrid electric vehicle powertrain, Part A: coupling bio-inspired techniques to meshless variable-fidelity surrogate models
by Ahmad Mozaffari; Maryyeh Chehresaz; Nasser L. Azad
International Journal of Bio-Inspired Computation (IJBIC), Vol. 5, No. 6, 2013

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 (P_EM-max) and maximum power of combustion engine (P_CE-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.

Online publication date: Mon, 31-Mar-2014

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