Title: Hybrid vehicle design using global optimisation algorithms

Authors: Wenzhong Gao, Chris Mi

Addresses: Department of Electrical and Computer Engineering, Center for Energy Systems Research, Tennessee Technological University, 1020 Stadium Dr, PH414, Box 5032, Cookeville, TN 38505, USA. ' Department of Electrical and Computer Engineering, University of Michigan, 4901 Evergreen Road, Dearborn, MI 48128 USA

Abstract: Four global optimisation algorithms are applied in the design optimisation of a hybrid electric vehicle (HEV). These four algorithms are: DIRECT, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimisation. The optimisation objective is to achieve maximum fuel economy, subject to the constraints of vehicle performance. The model in the loop methodology is adopted for our design process, in which a vehicle model named PSAT is used as the analysis tool. The design optimisation results and the performance of the four optimisation algorithms are compared. Our initial study shows that DIRECT and Simulated Annealing algorithms are efficient for the complex HEV engineering design problem.

Keywords: design optimisation; global optimisation algorithms; hybrid electric vehicles; PSAT; hybrid vehicles; vehicle design; simulated annealing; genetic algorithms; particle swarm optimisation; PSO; DIRECT; fuel economy; vehicle performance.

DOI: 10.1504/IJEHV.2007.014447

International Journal of Electric and Hybrid Vehicles, 2007 Vol.1 No.1, pp.57 - 70

Published online: 08 Jul 2007 *

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