Title: Particle swarm optimisation for hybrid electric drive-train sizing

Authors: Soren Ebbesen; Christian Dönitz; Lino Guzzella

Addresses: Institute for Dynamic Systems and Control, Sonneggstrasse 3, ML K 41, 8092 Zurich, Switzerland ' Institute for Dynamic Systems and Control, Sonneggstrasse 3, ML K 41, 8092 Zurich, Switzerland ' Institute for Dynamic Systems and Control, Sonneggstrasse 3, ML K 41, 8092 Zurich, Switzerland

Abstract: Electric hybridisation of vehicles aims at reducing fuel consumption but increases production costs. Hence, automobile manufacturers are confronted with the multi-objective optimisation problem of sizing the drive-train components. In this paper, we evaluated Particle Swarm Optimisation (PSO) for solving this problem. The results showed that PSO performs significantly better than competing methods. Parameter sensitivities indicated that the optimal solution, the vehicle performance constraints, and the preference between fuel consumption and production cost are intimately coupled. Finally, a Pareto analysis confirmed that a relatively small increase in cost accounts for a majority of the total fuel saving potential.

Keywords: PSO; particle swarm optimisation; HEVs; hybrid electric vehicles; drivetrain sizing; vehicle design; fuel consumption; cost; numerical algorithms; search methods.

DOI: 10.1504/IJVD.2012.047382

International Journal of Vehicle Design, 2012 Vol.58 No.2/3/4, pp.181 - 199

Available online: 18 Jun 2012 *

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