Title: Components sizing optimisation of hybrid electric heavy duty truck using multi-objective genetic algorithm

Authors: Fereydoon Diba; Ebrahim Esmailzadeh

Addresses: Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, ON, L1H7K4, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, ON, L1H7K4, Canada

Abstract: Components sizing optimisation of a novel architecture of hybrid drivetrain for line-haul truck has been considered. This drivetrain architecture employs a self-propelled trailer and the traction is shared between the tractor and trailer. The comprehensive model of the vehicle, including the hybrid electric drivetrain is developed. The drivetrain components have been optimised using multi-objective genetic algorithm to minimise three objective functions, namely, the acceleration time, fuel consumption and the drivetrain price. The overall efficiency of the optimised hybrid drivetrain has been evaluated using computer model simulations. Engineering economic analysis is performed to demonstrate the ownership cost of the proposed drivetrain when compared with the non-hybrid and the non-optimised hybrid drivetrain for heavy duty vehicles. The results show that the proposed drivetrain has a superior capability in reducing the fuel consumption and the ownership cost.

Keywords: hybrid electric truck; component sizing; self-propelled trailer; multi-objective optimisation; genetic algorithm.

DOI: 10.1504/IJHVS.2020.108734

International Journal of Heavy Vehicle Systems, 2020 Vol.27 No.3, pp.387 - 404

Received: 21 Feb 2017
Accepted: 05 Oct 2017
Published online: 30 Jul 2020 *

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